Google announced several new artificial intelligence (AI) features coming to Samsung’s just-released Galaxy S24 series smartphones at the Galaxy Unpacked event today in San Jose.
The advancements showcase the companies’ continued partnership to integrate Google’s AI technology into Samsung devices.
Experience Google Gemini On Samsung
The new Galaxy S24 line, including the S24, S24 Plus, and S24 Ultra models, will utilize Google’s Gemini AI models to enable next-generation capabilities.
Samsung apps like Notes, Voice Recorder, and Keyboard will leverage Gemini Pro to provide enhanced summarization and productivity features. The Galaxy S24 Gallery app will also gain Generative Edit powered by Imagen 2, Google’s text-to-image diffusion model, for helpful photo editing tools.
“With our latest advances in AI, we have the opportunity to enhance what billions of people already love about Android,” said Hiroshi Lockheimer, Google’s SVP of Platforms & Ecosystems.
Circle To Search
A highlight among the new features is Circle to Search, which lets users search for anything on their phone by simply circling or highlighting text, images, or videos.
The search results will include AI-generated overviews summarizing critical information, allowing users to quickly understand concepts without switching between apps.
Enhanced Messaging
Messaging is also getting an upgrade.
Google Messages on the Galaxy S24 will introduce features like Magic Compose, which suggests stylistic messages using AI, and Photomoji, which creates custom emojis from user photos using generative models.
These additions aim to make messaging more personalized and engaging.
Improved Android Auto
For drivers, Android Auto on the Galaxy S24 will automatically summarize long texts and suggest relevant actions like navigation or calling while on the road. The interface will also match the look and feel of the Samsung device for a seamless experience.
“We’re excited about all these new capabilities coming to Android and the Galaxy S24 series, and we have lots more to come,” said Lockheimer.
Integrating Google’s state-of-the-art AI into Samsung’s flagship smartphones illustrates the tech giants’ deepening relationship and their vision to advance the Android ecosystem through impactful innovation.
Google announced two updates today that utilize new AI capabilities to make searching more natural and intuitive.
The first new feature, Circle to Search, allows Android users to search anything on their screen by simply circling or highlighting it.
The second update enhances Google’s multisearch feature, which was first introduced in 2022. When users take a photo or screenshot and ask a question, they will get an “AI-powered overview” summarizing the most relevant information found across the web.
Here’s more about each of the features announced today.
A New Gesture-Based Search Method: Circle to Search
Google has developed a new feature for Android called Circle to Search that will change how people interact with content on their phones. With Circle to Search, users can circle, highlight, or tap text, images, or videos within apps to instantly search for related information without switching between applications.
This convenient in-app searching capability is launching globally on January 31st on Google’s newest Pixel 8 phones, the Pixel 8 Pro, and Samsung’s recently released Galaxy S24 series devices.
By allowing quick access to searches without interrupting the current task, Circle to Search aims to provide a more streamlined user experience, particularly when curiosity leads someone to look something up while already engaged in another activity on their phone.
The integration across apps has the potential to minimize context switching and disruption while enhancing the overall Android user experience.
Multisearch: A Step Forward with AI Integration
Multisearch was introduced to Google Lens in 2022, allowing users to combine image and text searches. However, new developments in AI have greatly expanded what multisearch can do. Now, you can point your camera at something, ask questions, and get AI-generated answers beyond visually identifying the object.
For example, if you find an open board game at a yard sale, you may wonder what it is and how it’s played. With multisearch, you can take a picture of it, ask your question, and get an AI-compiled summary from different internet sources.
This makes it much easier to learn about new things you encounter. The AI does the work of researching and piecing together relevant information for you.
The AI-powered overviews for multisearch launch this week in the U.S. in English.
In Summary
Google’s new “Circle to Search” and enhanced “Multisearch” represent progress in making search more intuitive and natural.
As these roll out, Android users, especially those with new Pixel and Galaxy devices, can expect a more fluid, interactive search experience. However, it’s important to think critically about the information these tools provide.
As AI evolves, the capabilities and impact of our digital tools will too.
OpenAI announced the creation of a new subscription plan called ChatGPT Team, a collaborative workspace for organizations to centralize their ChatGPT related work in a secure environment.
In the year after ChatGPT was announced there were fears that AI would eventually replace workers. ChatGPT Team shows a different view of AI in the workplace where AI performs like a competent intern that can perform tedious projects like visualizing data in a line graph and extracting insights from it.
ChatGPT Team
The new subscription tier offers three main features:
Advanced GPT Models
Collaboration with shared custom GPTs
Security in a dedicated and private workspace
The way ChatGPT Team is visualized is an AI team assistant for each employee that can quickly perform mundane tasks such as converting data into a line chart, change an image of a whiteboard into text data or to visualize data and convert it into three action items. Other suggested uses are as a coding assistant, email automation, and data analysis, the kinds of tasks that are tedious and cut into useful productivity time.
Rather than replace workers, ChatGPT Team is envisioned as a productivity booster that allows team members to do more faster.
ChatGPT Team Features
ChatGPT Team offers early access to new features, absolutely no use of business data for training, custom GPT collaboration and an admin panel for managing ChatGPT Team, plus access to (with higher message caps): DALL·E, Browsing, and GPT-4 with a 32k context window.
ChatGPT Team costs $25 per team member which is fairly inexpensive when it’s considered as a time-saving assistant for each employee.
The official announcement explains:
“Integrating AI into everyday organizational workflows can make your team more productive.
In a recent study by the Harvard Business School, employees at Boston Consulting Group who were given access to GPT-4 reported completing tasks 25% faster and achieved a 40% higher quality in their work as compared to their peers who did not have access.”
OpenAI published a response to The New York Times’ lawsuit by alleging that The NYTimes used manipulative prompting techniques in order to induce ChatGPT to regurgitate lengthy excerpts, stating that the lawsuit is based on misuse of ChatGPT in order to “cherry pick” examples for the lawsuit.
The New York Times Lawsuit Against OpenAI
The New York Times filed a lawsuit against OpenAI (and Microsoft) for copyright infringement alleging that ChatGPT “recites Times content verbatim” among other complaints.
The lawsuit introduced evidence showing how GPT-4 could output large amounts of New York Times content without attribution as proof that GPT-4 infringes on The New York Times content.
The accusation that GPT-4 is outputting exact copies of New York Times content is important because it counters OpenAI’s insistence that its use of data is transformative, which is a legal framework related to the doctrine of fair use.
“Fair use is a legal doctrine that promotes freedom of expression by permitting the unlicensed use of copyright-protected works in certain circumstances.
…’transformative’ uses are more likely to be considered fair. Transformative uses are those that add something new, with a further purpose or different character, and do not substitute for the original use of the work.”
That’s why it’s important for The New York Times to assert that OpenAI’s use of content is not fair use.
“Defendants insist that their conduct is protected as “fair use” because their unlicensed use of copyrighted content to train GenAI models serves a new “transformative” purpose. But there is nothing “transformative” about using The Times’s content …Because the outputs of Defendants’ GenAI models compete with and closely mimic the inputs used to train them, copying Times works for that purpose is not fair use.”
The following screenshot shows evidence of how GPT-4 outputs exact copy of the Times’ content. The content in red is original content created by the New York Times that was output by GPT-4.
OpenAI Response Undermines NYTimes Lawsuit Claims
OpenAI offered a strong rebuttal of the claims made in the New York Times lawsuit, claiming that the Times’ decision to go to court surprised OpenAI because they had assumed the negotiations were progressing toward a resolution.
Most importantly, OpenAI debunked The New York Times claims that GPT-4 outputs verbatim content by explaining that GPT-4 is designed to not output verbatim content and that The New York Times used prompting techniques specifically designed to break GPT-4’s guardrails in order to produce the disputed output, undermining The New York Times’ implication that outputting verbatim content is a common GPT-4 output.
This type of prompting that is designed to break ChatGPT in order to generate undesired output is known as Adversarial Prompting.
Adversarial Prompting Attacks
Generative AI is sensitive to the types of prompts (requests) made of it and despite the best efforts of engineers to block the misuse of generative AI there are still new ways of using prompts to generate responses that get around the guardrails built into the technology that are designed to prevent undesired output.
Techniques for generating unintended output is called Adversarial Prompting and that’s what OpenAI is accusing The New York Times of doing in order to manufacture a basis of proving that GPT-4 use of copyrighted content is not transformative.
OpenAI’s claim that The New York Times misused GPT-4 is important because it undermines the lawsuit’s insinuation that generating verbatim copyrighted content is typical behavior.
That kind of adversarial prompting also violates OpenAI’s terms of use which states:
What You Cannot Do
Use our Services in a way that infringes, misappropriates or violates anyone’s rights.
Interfere with or disrupt our Services, including circumvent any rate limits or restrictions or bypass any protective measures or safety mitigations we put on our Services.
OpenAI Claims Lawsuit Based On Manipulated Prompts
OpenAI’s rebuttal claims that the New York Times used manipulated prompts specifically designed to subvert GPT-4 guardrails in order to generate verbatim content.
OpenAI writes:
“It seems they intentionally manipulated prompts, often including lengthy excerpts of articles, in order to get our model to regurgitate.
Even when using such prompts, our models don’t typically behave the way The New York Times insinuates, which suggests they either instructed the model to regurgitate or cherry-picked their examples from many attempts.”
OpenAI also fired back at The New York Times lawsuit saying that the methods used by The New York Times to generate verbatim content was a violation of allowed user activity and misuse.
They write:
“Despite their claims, this misuse is not typical or allowed user activity.”
OpenAI ended by stating that they continue to build resistance against the kinds of adversarial prompt attacks used by The New York Times.
They write:
“Regardless, we are continually making our systems more resistant to adversarial attacks to regurgitate training data, and have already made much progress in our recent models.”
OpenAI backed up their claim of diligence to respecting copyright by citing their response to July 2023 to reports that ChatGPT was generating verbatim responses.
We’ve learned that ChatGPT’s “Browse” beta can occasionally display content in ways we don’t want, e.g. if a user specifically asks for a URL’s full text, it may inadvertently fulfill this request. We are disabling Browse while we fix this—want to do right by content owners.
There’s always two sides of a story and OpenAI just released their side that shows that The New York Times claims are based on adversarial attacks and a misuse of ChatGPT in order to elicit verbatim responses.
Read OpenAIs response:
OpenAI and journalism: We support journalism, partner with news organizations, and believe The New York Times lawsuit is without merit.
Researchers tested the idea that an AI model may have an advantage in self-detecting its own content because the detection was leveraging the same training and datasets. What they didn’t expect to find was that out of the three AI models they tested, the content generated by one of them was so undetectable that even the AI that generated it couldn’t detect it.
The study was conducted by researchers from the Department of Computer Science, Lyle School of Engineering at Southern Methodist University.
AI Content Detection
Many AI detectors are trained to look for the telltale signals of AI generated content. These signals are called “artifacts” which are generated because of the underlying transformer technology. But other artifacts are unique to each foundation model (the Large Language Model the AI is based on).
These artifacts are unique to each AI and they arise from the distinctive training data and fine tuning that is always different from one AI model to the next.
The researchers discovered evidence that it’s this uniqueness that enables an AI to have a greater success in self-identifying its own content, significantly better than trying to identify content generated by a different AI.
Bard has a better chance of identifying Bard-generated content and ChatGPT has a higher success rate identifying ChatGPT-generated content, but…
The researchers discovered that this wasn’t true for content that was generated by Claude. Claude had difficulty detecting content that it generated. The researchers shared an idea of why Claude was unable to detect its own content and this article discusses that further on.
This is the idea behind the research tests:
“Since every model can be trained differently, creating one detector tool to detect the artifacts created by all possible generative AI tools is hard to achieve.
Here, we develop a different approach called self-detection, where we use the generative model itself to detect its own artifacts to distinguish its own generated text from human written text.
This would have the advantage that we do not need to learn to detect all generative AI models, but we only need access to a generative AI model for detection.
This is a big advantage in a world where new models are continuously developed and trained.”
Methodology
The researchers tested three AI models:
ChatGPT-3.5 by OpenAI
Bard by Google
Claude by Anthropic
All models used were the September 2023 versions.
A dataset of fifty different topics was created. Each AI model was given the exact same prompts to create essays of about 250 words for each of the fifty topics which generated fifty essays for each of the three AI models.
Each AI model was then identically prompted to paraphrase their own content and generate an additional essay that was a rewrite of each original essay.
They also collected fifty human generated essays on each of the fifty topics. All of the human generated essays were selected from the BBC.
The researchers then used zero-shot prompting to self-detect the AI generated content.
Zero-shot prompting is a type of prompting that relies on the ability of AI models to complete tasks for which they haven’t specifically trained to do.
The researchers further explained their methodology:
“We created a new instance of each AI system initiated and posed with a specific query: ‘If the following text matches its writing pattern and choice of words.’ The procedure is repeated for the original, paraphrased, and human essays, and the results are recorded.
We also added the result of the AI detection tool ZeroGPT. We do not use this result to compare performance but as a baseline to show how challenging the detection task is.”
They also noted that a 50% accuracy rate is equal to guessing which can be regarded as essentially a level of accuracy that is a failure.
Results: Self-Detection
It must be noted that the researchers acknowledged that their sample rate was low and said that they weren’t making claims that the results are definitive.
Below is a graph showing the success rates of AI self-detection of the first batch of essays. The red values represent the AI self-detection and the blue represents how well the AI detection tool ZeroGPT performed.
Results Of AI Self-Detection Of Own Text Content
Bard did fairly well at detecting its own content and ChatGPT also performed similarly well at detecting its own content.
ZeroGPT, the AI detection tool detected the Bard content very well and performed slightly less better in detecting ChatGPT content.
ZeroGPT essentially failed to detect the Claude-generated content, performing worse than the 50% threshold.
Claude was the outlier of the group because it was unable to to self-detect its own content, performing significantly worse than Bard and ChatGPT.
The researchers hypothesized that it may be that Claude’s output contains less detectable artifacts, explaining why both Claude and ZeroGPT were unable to detect the Claude essays as AI-generated.
So, although Claude was unable to reliably self-detect its own content, that turned out to be a sign that the output from Claude was of a higher quality in terms of outputting less AI artifacts.
ZeroGPT performed better at detecting Bard-generated content than it did in detecting ChatGPT and Claude content. The researchers hypothesized that it could be that Bard generates more detectable artifacts, making Bard easier to detect.
So in terms of self-detecting content, Bard may be generating more detectable artifacts and Claude is generating less artifacts.
Results: Self-Detecting Paraphrased Content
The researchers hypothesized that AI models would be able to self-detect their own paraphrased text because the artifacts that are created by the model (as detected in the original essays) should also be present in the rewritten text.
However the researchers acknowledged that the prompts for writing the text and paraphrasing are different because each rewrite is different than the original text which could consequently lead to a different self-detection results for the self-detection of paraphrased text.
The results of the self-detection of paraphrased text was indeed different from the self-detection of the original essay test.
Bard was able to self-detect the paraphrased content at a similar rate.
ChatGPT was not able to self-detect the paraphrased content at a rate much higher than the 50% rate (which is equal to guessing).
ZeroGPT performance was similar to the results in the previous test, performing slightly worse.
Perhaps the most interesting result was turned in by Anthropic’s Claude.
Claude was able to self-detect the paraphrased content (but it was not able to detect the original essay in the previous test).
It’s an interesting result that Claude’s original essays apparently had so few artifacts to signal that it was AI generated that even Claude was unable to detect it.
Yet it was able to self-detect the paraphrase while ZeroGPT could not.
The researchers remarked on this test:
“The finding that paraphrasing prevents ChatGPT from self-detecting while increasing Claude’s ability to self-detect is very interesting and may be the result of the inner workings of these two transformer models.”
Screenshot of Self-Detection of AI Paraphrased Content
These tests yielded almost unpredictable results, particularly with regard to Anthropic’s Claude and this trend continued with the test of how well the AI models detected each others content, which had an interesting wrinkle.
Results: AI Models Detecting Each Other’s Content
The next test showed how well each AI model was at detecting the content generated by the other AI models.
If it’s true that Bard generates more artifacts than the other models, will the other models be able to easily detect Bard-generated content?
The results show that yes, Bard-generated content is the easiest to detect by the other AI models.
Regarding detecting ChatGPT generated content, both Claude and Bard were unable to detect it as AI-generated (justa as Claude was unable to detect it).
ChatGPT was able to detect Claude-generated content at a higher rate than both Bard and Claude but that higher rate was not much better than guessing.
The finding here is that all of them weren’t so good at detecting each others content, which the researchers opined may show that self-detection was a promising area of study.
Here is the graph that shows the results of this specific test:
At this point it should be noted that the researchers don’t claim that these results are conclusive about AI detection in general. The focus of the research was testing to see if AI models could succeed at self-detecting their own generated content. The answer is mostly yes, they do a better job at self-detecting but the results are similar to what was found with ZEROGpt.
The researchers commented:
“Self-detection shows similar detection power compared to ZeroGPT, but note that the goal of this study is not to claim that self-detection is superior to other methods, which would require a large study to compare to many state-of-the-art AI content detection tools. Here, we only investigate the models’ basic ability of self detection.”
Conclusions And Takeaways
The results of the test confirm that detecting AI generated content is not an easy task. Bard is able to detect its own content and paraphrased content.
ChatGPT can detect its own content but works less well on its paraphrased content.
Claude is the standout because it’s not able to reliably self-detect its own content but it was able to detect the paraphrased content, which was kind of weird and unexpected.
Detecting Claude’s original essays and the paraphrased essays was a challenge for ZeroGPT and for the other AI models.
The researchers noted about the Claude results:
“This seemingly inconclusive result needs more consideration since it is driven by two conflated causes.
1) The ability of the model to create text with very few detectable artifacts. Since the goal of these systems is to generate human-like text, fewer artifacts that are harder to detect means the model gets closer to that goal.
2) The inherent ability of the model to self-detect can be affected by the used architecture, the prompt, and the applied fine-tuning.”
The researchers had this further observation about Claude:
“Only Claude cannot be detected. This indicates that Claude might produce fewer detectable artifacts than the other models.
The detection rate of self-detection follows the same trend, indicating that Claude creates text with fewer artifacts, making it harder to distinguish from human writing”.
But of course, the weird part is that Claude was also unable to self-detect its own original content, unlike the other two models which had a higher success rate.
The researchers indicated that self-detection remains an interesting area for continued research and propose that further studies can focus on larger datasets with a greater diversity of AI-generated text, test additional AI models, a comparison with more AI detectors and lastly they suggested studying how prompt engineering may influence detection levels.
Read the original research paper and the abstract here:
Celebrate the Holidays with some of SEJ’s best articles of 2023.
Our Festive Flashback series runs from December 21 – January 5, featuring daily reads on significant events, fundamentals, actionable strategies, and thought leader opinions.
2023 has been quite eventful in the SEO industry, and our contributors produced some outstanding articles to keep pace and reflect these changes.
Catch up on the best reads of 2023 to give you plenty to reflect on as you move into 2024.
ChatGPT for keyword research does not replace traditional keyword research tools.
And just like keyword research tools, you shouldn’t unthinkingly rely on them alone to do your keyword research.
Instead, I will show you in this post how ChatGPT can be one of the best free keyword research tools you should add to your SEO toolbox.
Save countless hours going down competitor websites, keyword rabbit holes, and topic analysis.
Steal my most effective prompts for conducting keyword research – and if you can hold off on trying them out and get to the end of the article, I will provide you with some additional advanced use cases for ChatGPT and keyword research.
Let’s jump straight into what you care about the most: the prompts.
Best ChatGPT Keyword Research Prompts
The beauty of the following ChatGPT keyword research prompts is that they can be used on any niche, even a topic to which you are brand new.
However, for this demonstration, let’s use the topic of “SEO” to demonstrate these prompts.
Generating Keyword Ideas Based On A Topic
What Are The {X} Most Popular Sub-topics Related To {Topic}?
Screenshot from ChatGPT, March 2023
The first prompt is to give you an idea of the niche.
As shown above, ChatGPT did a great job understanding and breaking down SEO into three pillars: on-page, off-page & technical.
The key to the following prompt is to take one of the topics ChatGPT has given and query the sub-topics.
What Are The {X} Most Popular Sub-topics Related To {Sub-topic}?
For this example, let’s query, “what are the most popular sub-topics related to keyword research”.
Having done keyword research for over 10 years, I would expect it to output information related to keyword research metrics, the types of keywords, and intent.
Let’s see.
Screenshot from ChatGPT, March 2023
Again, right on the money.
Now, to get the keywords.
But to reiterate, this guide on ChatGPT keyword research is not to get you to replace traditional tools; it is to show how you can leverage ChatGPT to give you ideas to plug into those tools to verify.
To get the keywords you want without having ChatGPT describe each answer, use the prompt “list without description.”
Here is an example of that.
List Without Description The Top {X} Most Popular Keywords For The Topic Of {X}
Screenshot from ChatGPT, March 2023
You can even branch these keywords out further into their long-tail.
Example prompt:
List Without Description The Top {X} Most Popular Long-tail Keywords For The Topic “{X}”
Screenshot from ChatGPT, March 2023
List Without Description The Top Semantically Related Keywords And Entities For The Topic {X}
Who needs those expensive content optimization tools? You can even ask ChatGPT what any topic’s semantically related keywords and entities are!
Screenshot from ChatGPT, March 2023
Tip: The Onion Method Of Prompting ChatGPT
When you are happy with a series of prompts, add them all to one prompt. For example, so far in this article, we have asked ChatGPT the following:
What are the four most popular sub-topics related to SEO?
What are the four most popular sub-topics related to keyword research
List without description the top five most popular keywords for “keyword intent”?
List without description the top five most popular long-tail keywords for the topic “keyword intent types”?
List without description the top semantically related keywords and entities for the topic “types of keyword intent in SEO.”
Take all five, and combine them into one prompt by telling ChatGPT to perform a series of steps. Example;
“Perform the following steps in a consecutive order Step 1, Step 2, Step 3, Step 4, and Step 5”
Example:
“Perform the following steps in a consecutive order Step 1, Step 2, Step 3, Step 4 and Step 5. Step 1 – Generate an answer for the 3 most popular sub-topics related to {Topic}?. Step 2 – Generate 3 of the most popular sub-topics related to each answer. Step 3 – Take those answers and list without description their top 3 most popular keywords. Step 4 – For the answers given of their most popular keywords, provide 3 long-tail keywords. Step 5 – for each long-tail keyword offered in the response, a list without descriptions 3 of their top semantically related keywords and entities.”
Generating Keyword Ideas Based On A Question
Taking the steps approach from above, we can get ChatGPT to help streamline getting keyword ideas based on a question. For example, let’s ask, “What is SEO?”
“Perform the following steps in a consecutive order Step 1, Step 2, Step 3, and Step 4. Step 1 Generate 10 questions about “{Question}”?. Step 2 – Generate 5 more questions about “{Question}” that do not repeat the above. Step 3 – Generate 5 more questions about “{Question}” that do not repeat the above. Step 4 – Based on the above Steps 1,2,3 suggest a final list of questions avoiding duplicates or semantically similar questions.”
Screenshot from ChatGPT, March 2023
Generating Keyword Ideas Using ChatGPT Based On The Alphabet Soup Method
One of my favorite methods, manually, without even using a keyword research tool, is to generate keyword research ideas from Google autocomplete, going from a to z.
Screenshot from Google.com Autocomplete, March 2023
You can also do this using ChatGPT.
Example prompt:
“give me popular keywords that includes the keyword ‘SEO,’ and the next letter of the word starts with a”
Screenshot from ChatGPT, March 2023
Tip: Using the onion prompting method above, we can combine all this in one prompt.
“Give me five popular keywords that include ‘SEO’ in the word, and the following letter starts with a. Once the answer has been done, move on to giving five more popular keywords that include ‘SEO’ for each letter of the alphabet b to z.”
Screenshot from ChatGPT, March 2023
Generating Keyword Ideas Based On User Personas
When it comes to keyword research, understanding user personas is essential for understanding your target audience and keeping your keyword research focused and targeted. ChatGPT may help you get an initial understanding of customer personas.
Example prompt:
“For the topic of “{Topic}” list 10 keywords each for the different types of user personas”
Screenshot from ChatGPT, March 2023
You could even go a step further and ask for questions based on those topics that those specific user personas may be searching for:
Screenshot from ChatGPT, March 2023
As well as get the keywords to target based on those questions:
“For each question listed above for each persona, list the keywords, as well as the long-tail keywords to target, and put them in a table”
Screenshot from ChatGPT, March 2023
Generating Keyword Ideas Using ChatGPT Based On Searcher Intent And User Personas
Understanding the keywords your target persona may be searching is the first step to effective keyword research. The next step is to understand the search intent behind those keywords and which content format may work best.
For example, a business owner who is new to SEO or has just heard about it may be searching for “what is SEO.”
However, if they are further down the funnel and in the navigational stage, they may search for “top SEO firms.”
You can query ChatGPT to inspire you here based on any topic and your target user persona.
SEO Example:
“For the topic of “{Topic}” list 10 keywords each for the different types of searcher intent that a {Target Persona} would be searching for”
Screenshot from ChatGPT, March 2023
ChatGPT As Your Keyword Research Assistant
Some of you may still be on the fence about using ChatGPT in your daily SEO workflows. However, I can honestly tell you that one of the three windows I have open daily doing SEO work is ChatGPT.
In particular for idea generation – as shown above – but also for time-consuming spreadsheet tasks.
In the past, I would have had to devise spreadsheet formulas to categorize keywords or even spend hours filtering and manually categorizing keywords.
ChatGPT can be a great companion to run a short version of this for you.
Let’s say you have done keyword research in a keyword research tool, have a list of keywords, and want to categorize them.
You could use the following prompt:
“Filter the below list of keywords into categories, target persona, searcher intent, search volume and add information to a six-column table: List of keywords – [LIST OF KEYWORDS], Keyword Search Volume [SEARCH VOLUMES] and Keyword Difficulties [KEYWORD DIFFICUTIES].”
Screenshot from ChatGPT, March 2023
Tip: Add keyword metrics from the keyword research tools, as using the search volumes that a ChatGPT prompt may give you will be wildly inaccurate at best.
Using Chat GPT For Keyword Clustering
Another of ChatGPT’s use cases for keyword research is to help you cluster. Many keywords have the same intent, and by grouping related keywords, you may find more often than not that one piece of content can target multiple keywords at once.
A great use case for this is for clustering People Also Ask (PAA) questions.
Let’s use the free SEO Minion plugin to extract all PAAs for the question, “What is SEO?”
Then, using the following prompt, we can group keywords based on their semantic relationships. For example:
“Organize the following keywords into groups based on their semantic relationships, and give a short name to each group: [LIST OF PAA], create a two-column table where each keyword sits on its own row.
Screenshot from ChatGPT, March 2023
Using Chat GPT For Keyword Expansion By Patterns
One of my favorite methods of doing keyword research is pattern spotting.
Most seed keywords have a variable that can expand your target keywords.
“Generate [X] keywords for the topic “[Topic]” that contain any or all of the following “who, what, where, why, how, are, can, do, does, will”
Screenshot from ChatGPT, March 2023
2. Comparison Patterns
Example:
“Generate 50 keywords for the topic “SEO” that contain any or all of the following “for, vs, alternative, best, top, review”
Screenshot from ChatGPT, March 2023
Or let’s say you were striving for “topical authority“; you could take one of those modifiers and get a full list of comparison queries for one variable.
Example:
“Generate 50 keywords for the topic “[Topic]” that contain “vs”
Screenshot from ChatGPT, March 2023
3. Brand Patterns
Another one of my favorite modifiers is a keyword by brand.
We are probably all familiar with the most popular SEO brands; however, if you weren’t, you could lean on your AI friend to do the heavy lifting.
Example prompt:
“For the top {Topic} brands what are the top “vs” keywords”
Screenshot from ChatGPT, March 2023
4. Search Intent Patterns
One of the most common search intent patterns is “best.” When someone is searching for a “best {topic}” keyword, they are generally searching for a comprehensive list or guide that highlights the top options, products, or services within that specific topic, along with their features, benefits, and potential drawbacks, to make an informed decision.
Example:
“For the topic of “[Topic]” what are the 20 top keywords that include “best”
Screenshot from ChatGPT, March 2023
Again, this guide to keyword research using ChatGPT has emphasized the ease of generating keyword research ideas by utilizing ChatGPT throughout the process.
Keyword Research Using ChatGPT Vs. Keyword Research Tools
Free Vs. Paid Keyword Research Tools
Just like keyword research tools, there are free and paid options for ChatGPT.
But one of the most significant drawbacks of using ChatGPT for keyword research alone is the absence of SEO metrics to help you make smarter decisions.
The other downside of ChatGPT is that it is only in its infancy, and the keywords it suggests may be inaccurate.
To improve accuracy, you could take the results it gives you and verify them with your classic keyword research tool – or vice versa, as shown above, uploading accurate data into the tool and then prompting.
However, you must consider how long it takes to type and fine-tune your prompt to get your desired data versus using the filters within popular keyword research tools.
For example, if we use a popular keyword research tool using filters, you could have all of the “best” queries with all of their SEO metrics:
Screenshot from Ahrefs Keyword Explorer, March 2023
And unlike ChatGPT, generally, there is no token limit; you can extract several hundred, if not thousands, of keywords at a time.
That is not to say that ChatGPT does not have its place in keyword research; quite the contrary. It can be your new super-intelligent SEO assistant, giving you endless ideas.
The key is how you prompt. And, hopefully, the prompts I have shared with you will give you a glimpse into how ChatGPT can help with keyword research.
Bonus: Enhancing Keyword Research Using The Chat GPT API (Open AI API)
As promised at the start, if you made it to the end of this article, I would share an advanced use case for ChatGPT and keyword research.
And if you have read my previous guide, you will know that doing keyword research for news websites is different. It is all about trending keywords – and sometimes, keywords that have never been searched.
Sometimes, people can be prompted to search based on seeing something on social. This is why social media listening tools can be a great way to get insights into trends and what people may be searching for.
Well, what if you could automate some of that process?
Celebrate the Holidays with some of SEJ’s best articles of 2023.
Our Festive Flashback series runs from December 21 – January 5, featuring daily reads on significant events, fundamentals, actionable strategies, and thought leader opinions.
2023 has been quite eventful in the SEO industry and our contributors produced some outstanding articles to keep pace and reflect these changes.
Catch up on the best reads of 2023 to give you plenty to reflect on as you move into 2024.
With the advent of AI-powered language models like ChatGPT, how well you write prompts determines the quality of the results you get from the tool.
You can instruct ChatGPT to generate information, social media posts, product descriptions, and more by providing a few keywords or phrases.
Whether you are a digital marketer, blogger, or business owner, mastering the art of writing prompts is essential to creating compelling content that resonates with your audience.
Writing effective prompts can be challenging because the quality of the output depends on the instructions’ specificity and clarity.
A ChatGPT prompt is an instruction or discussion topic a user provides for the ChatGPT AI model to respond to.
The prompt can be a question, statement, or any other stimulus intended to spark creativity, reflection, or engagement.
Users can use the prompt to generate ideas, share their thoughts, or start a conversation.
ChatGPT prompts are designed to be open-ended and can be customized based on the user’s preferences and interests.
How To Write Prompts For ChatGPT
Start by giving ChatGPT a writing prompt, such as “Write a short story about a person who discovers they have a superpower.”
ChatGPT will then generate a response based on your prompt. The answer may be a few sentences or several paragraphs long, depending on the prompt’s complexity and the level of detail you requested.
Use the ChatGPT-generated response as a starting point for your writing. You can take the ideas and concepts presented in the answer and expand on them, adding your own unique spin to the story.
If you want to generate additional ideas, try asking ChatGPT follow-up questions related to your original prompt.
For example, you could ask, “What challenges might the person face in exploring their newfound superpower?” or “How might the person’s relationships with others be affected by their superpower?”
Remember that ChatGPT’s answers are generated by artificial intelligence and may not always be perfect or exactly what you want.
However, they can still be a great source of inspiration and help you start writing.
Must-Have Chrome Plugin
I recommend installing the WebChatGPT plugin, which allows you to add relevant results from Google to your ChatGPT prompts.
This extension adds the first web results to your ChatGPT prompts for more accurate and up-to-date conversations.
Screenshot from ChatGPT, March 2023
For example, if I asked, “Who is Vincent Terrasi?,” ChatGPT has no answer.
With WebChatGPT On, the Chrome plugin creates a new prompt with the first Google results, and now ChatGPT knows who Vincent Terrasi is.
But the hallucination is still there because I never worked for Hilti; ChatGPT invented the company because it didn’t have the data.
Screenshot from ChatGPT, March 2023
Master Reverse Prompt Engineering
ChatGPT can be an excellent tool for reverse engineering prompts because it generates natural and engaging responses to a given input.
By analyzing the prompts generated by ChatGPT, it is possible to gain insight into the model’s underlying thought processes and decision-making strategies.
One of the key benefits of using ChatGPT to reverse engineer prompts is that the model is highly transparent in its decision-making.
This means that it is possible to trace the reasoning and logic behind each response, making it easier to understand how the model arrives at its conclusions.
Once you’ve done this a few times for different types of content, you’ll gain insight on crafting more effective prompts.
Prepare Your ChatGPT For Generating Prompts
First, activate the reverse prompt engineering.
Type the following prompt: “Enable Reverse Prompt Engineering? By Reverse Prompt Engineering I mean creating a prompt from a given text.”
Screenshot from ChatGPT, March 2023
Ok, ChatGPT is now ready to generate your prompt. You can test the product description in a new chatbot session and evaluate the generated prompt.
Type: “Create a very technical reverse prompt engineering template for a product description about iPhone 11.”
Screenshot from ChatGPT, March 2023
The result is amazing. You can test with a full text that you want to reproduce. Here is an example of a prompt for selling a Kindle on Amazon.
Type: “Reverse Prompt engineer the following {product), capture the writing style and the length of the text : product =”
Screenshot from ChatGPT, March 2023
I tested it on an SEJ blog post. Enjoy the analysis – it is excellent.
Type: “Reverse Prompt engineer the following {text}, capture the tone and writing style of the {text} to include in the prompt : text = all text coming from https://www.searchenginejournal.com/google-bard-training-data/478941/”
Screenshot from ChatGPT, March 2023
But be careful that you don’t use ChatGPT for generating your texts. It is just a personal assistant.
Is every answer generated by ChatGPT really unique? Or are we overestimating its ability to produce different texts?
This is the fascinating question that arose after I analyzed 10,000 texts produced by ChatGPT.
Conclusion
In conclusion, the study of the text quality generated by ChatGPT has produced some interesting results.
While the algorithm can produce similar answers to different questions, there are questions about the promise of OpenAI.
It appears that ChatGPT may not be the best tool for generating content due to a lack of creativity and significant duplication of content.
However, the tool may still be useful for finding the perfect prompt for generating qualitative text using other generators such as LLAMA, OPT, BLOOM, GPT3.5, or Cohere.
Many are aware of the popular Chain of Thoughts (CoT) method of prompting generative AI in order to obtain better and more sophisticated responses. Researchers from Google DeepMind and Princeton University developed an improved prompting strategy called Tree of Thoughts (ToT) that takes prompting to a higher level of results, unlocking more sophisticated reasoning methods and better outputs.
The researchers explain:
“We show how deliberate search in trees of thoughts (ToT) produces better results, and more importantly, interesting and promising new ways to use language models to solve problems requiring search or planning.”
Researchers Compare Against Three Kinds Of Prompting
The research paper compares ToT against three other prompting strategies.
1. Input-output (IO) Prompting This is basically giving the language model a problem to solve and getting the answer.
An example based on text summarization is:
Input Prompt: Summarize the following article. Output Prompt: Summary based on the article that was input
2. Chain Of Thought Prompting
This form of prompting is where a language model is guided to generate coherent and connected responses by encouraging it to follow a logical sequence of thoughts. Chain-of-Thought (CoT) Prompting is a way of guiding a language model through the intermediate reasoning steps to solve problems.
Question: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now? Reasoning: Roger started with 5 balls. 2 cans of 3 tennis balls each is 6 tennis balls. 5 + 6 = 11. The answer: 11
Question: The cafeteria had 23 apples. If they used 20 to make lunch and bought 6 more, how many apples do they have?
3. Self-consistency with CoT
In simple terms, this is a prompting strategy of prompting the language model multiple times then choosing the most commonly arrived at answer.
The research paper on Sel-consistency with CoT from March 2023 explains it:
“It first samples a diverse set of reasoning paths instead of only taking the greedy one, and then selects the most consistent answer by marginalizing out the sampled reasoning paths. Self-consistency leverages the intuition that a complex reasoning problem typically admits multiple different ways of thinking leading to its unique correct answer.”
Dual Process Models in Human Cognition
The researchers take inspiration from a theory of how human decision thinking called dual process models in human cognition or dual process theory.
Dual process models in human cognition proposes that humans engage in two kinds of decision-making processes, one that is intuitive and fast and another that is more deliberative and slower.
Fast, Automatic, Unconscious This mode involves fast, automatic, and unconscious thinking that’s often said to be based on intuition.
Slow, Deliberate, Conscious This mode of decision-making is a slow, deliberate, and conscious thinking process that involves careful consideration, analysis, and step by step reasoning before settling on a final decision.
The Tree of Thoughts (ToT) prompting framework uses a tree structure of each step of the reasoning process that allows the language model to evaluate each reasoning step and decide whether or not that step in the reasoning is viable and lead to an answer. If the language model decides that the reasoning path will not lead to an answer the prompting strategy requires it to abandon that path (or branch) and keep moving forward with another branch, until it reaches the final result.
Tree Of Thoughts (ToT) Versus Chain of Thoughts (CoT)
The difference between ToT and and CoT is that ToT is has a tree and branch framework for the reasoning process whereas CoT takes a more linear path.
In simple terms, CoT tells the language model to follow a series of steps in order to accomplish a task, which resembles the system 1 cognitive model that is fast and automatic.
ToT resembles the system 2 cognitive model that is more deliberative and tells the language model to follow a series of steps but to also have an evaluator step in and review each step and if it’s a good step to keep going and if not to stop and follow another path.
Illustrations Of Prompting Strategies
The research paper published schematic illustrations of each prompting strategy, with rectangular boxes that represent a “thought” within each step toward completing the task, solving a problem. The following is a screenshot of what the reasoning process for ToT looks like:
Illustration of Chain of Though Prompting
This is the schematic illustration for CoT, showing how the thought process is more of a straight path (linear):
The research paper explains:
“Research on human problem-solving suggests that people search through a combinatorial problem space – a tree where the nodes represent partial solutions, and the branches correspond to operators that modify them. Which branch to take is determined by heuristics that help to navigate the problem-space and guide the problem-solver towards a solution.
This perspective highlights two key shortcomings of existing approaches that use LMs to solve general problems:
1) Locally, they do not explore different continuations within a thought process – the branches of the tree.
2) Globally, they do not incorporate any type of planning, lookahead, or backtracking to help evaluate these different options – the kind of heuristic-guided search that seems characteristic of human problem-solving.
To address these shortcomings, we introduce Tree of Thoughts (ToT), a paradigm that allows LMs to explore multiple reasoning paths over thoughts…”
Tested With A Mathematical Game
The researchers tested the method using a Game of 24 math game. Game of 24 is a mathematical card game where players use four numbers (that can only be used once) from a set of cards to combine them using basic arithmetic (addition, subtraction, multiplication, and division) to achieve a result of 24.
Results and Conclusions
The researchers tested the ToT prompting strategy against the three other approaches and found that it produced consistently better results.
However they also note that ToT may not be necessary for completing tasks that GPT-4 already does well at.
They conclude:
“The associative “System 1” of LMs can be beneficially augmented by a “System 2″ based on searching a tree of possible paths to the solution to a problem.
The Tree of Thoughts framework provides a way to translate classical insights about problem-solving into actionable methods for contemporary LMs.
At the same time, LMs address a weakness of these classical methods, providing a way to solve complex problems that are not easily formalized, such as creative writing.
We see this intersection of LMs with classical approaches to AI as an exciting direction.”
John Mueller, Google Search Advocate, recently shared his “shower” thoughts on the use of AI-generated images on websites versus stock photography.
His discussion opened up an intriguing debate on how users perceive images created with generative AI tools like DALL·E, especially in contexts that are not primarily focused on art or AI.
The post included a disclaimer it is not meant to serve as SEO advice or foreshadowing of an upcoming Google search update.
AI-Generated Images Vs. Stock Photography
Mueller begins by distinguishing between situations where a specific photograph is necessary and those where imagery serves as mere decoration.
He argues that in some circumstances, like a suitcase a website aims to sell, authentic photographs are essential.
While real photos might undergo digital enhancements or editing, the foundation of product photography must be rooted in reality to provide consumers with an accurate representation of a future investment.
On the other hand, Mueller points out that for general content embellishment, there is little difference between using stock photography and AI-generated images.
Both types of imagery can enhance the aesthetic appeal of a website, making the content more engaging and enjoyable for the reader.
This distinction underlines that the decision to use real photos versus AI-generated images largely depends on the specific needs and goals of the website content.
The Value Of Images For User Experience
Mueller also touches upon the relevance of the subject matter of the website.
He suggests that for certain topics, audiences expect real images, while for others, the distinction between real and AI-generated images may be negligible.
This expectation ties into search engine optimization (SEO), as Mueller hypothesizes that users are more inclined to search visually for topics where real images are valued.
Further, Mueller offers practical advice for website owners considering the use of AI-generated images.
He encourages them to reflect on whether they would typically use stock photography in the same context. This approach can help in making an informed decision about the appropriateness of AI-generated images for their website.
Quality Standards Of AI-Generated Images
Mueller also cautions about the ease and temptation of using AI-generated images as a time and cost-saving measure.
He notes that taking a quick photo with a phone could be considered as creating ‘stock photography,’ but this might not meet the professional standards expected on a business website.
He emphasizes that quality and professionalism often require time and experience.
AI Images, AR Models, And Consumer Trust
Throughout the comments, Mueller answered questions about images, AI, and SEO. Here are some of the best responses.
Should you add rel=nofollow for an image credit link?
“Links are fine. No need to use rel=nofollow if they’re normal links.”
AR For 3D Modeling
Mueller expressed a desire for augmented reality (AR) support in online product displays, emphasizing the value of using 3D models.
“Seeing a photo is a good start, trying it out in my own space is so much better.”
He also differentiated between 3D-rendered images based on actual building plans and fully AI-generated images, likening the latter to decorative blog post imagery.
Decorative Images & Real Product Photography
Regarding conceptual illustrations, Mueller noted that decorative images indicated the level of effort put into the content, enhancing user trust.
However, he criticizes the use of AI images for product photos, comparing it to low-quality imported product sites where photoshopped images often lead to unrealistic representations.
“…if you have the product, why not get real photos, and if you don’t have the product, you wouldn’t be able to confirm that the image is ok.”
AI-Generated Images As ‘Low-Effort’ Content
Considering that creative visualizations and real product photos are considered indicators of high-quality content, it’s no surprise that some uses of AI-generated images could be considered the opposite.
Mueller also offered another perspective: if real images represent an original source of content, AI images could represent scraped content.
“If I noticed a recipe site were using AI-generated images, I’d assume all of the content is scraped spam and just go somewhere else.”
AI Content Decreases Consumer Trust
When visitors discover content has been “faked,” it could harm their trust in anything else on the website. Mueller suggested that even an obvious “team” stock photo was less deceptive than one created by AI.
He acknowledged the value of good stock photography over a unique smartphone photo and how the latter did not equal professional-quality content.
But he is also aware that the lines are blurred more now that companies like Getty and Shutterstock have launched AI tools trained on licensed stock photography.
Conclusion
The discussion on Mueller’s LinkedIn post is particularly relevant, highlighting the evolving role of AI tools in content creation and its impact on user experience and SEO.
As marketers continually adapt to new technologies, understanding these nuances is crucial for effective digital marketing strategies. It prompts us to consider the authenticity of our visual content and its alignment with our audience’s expectations.
It’s essential to strike a balance between authenticity, professionalism, and the practical benefits of AI-generated images, keeping in mind the nature of the content and audience expectations.
In 2023, AI became a part of the user interfaces of top search engines, social media networks, advertising platforms, productivity software, and SEO tools.
After a year of non-stop breaking news about generative AI research, large language models (LLMs), stealth AI startups, GPT wrappers, and AI integrations, it may be hard to determine which solution is best suited for your objectives.
But if you make a list of challenges you face throughout your workday, you’ll find there is – or soon will be – an AI that makes handling those challenges simpler.
This quick guide offers over 100 of the best AI chatbots, tools, solutions, and training for everyone in SEO, from individual consultants to enterprise marketing teams.
Why AI Matters For SEO
Simply put, AI is or will be in just about everything you and your clients use.
Search engines like Google have been using AI for decades in ranking algorithms.
Bing offers AI-powered chat, summaries, and image creation capabilities.
Google is experimenting with similar features in the Search Generative Experience (SGE) and Notes.
The top advertising, business, marketing, productivity, and SEO platforms are integrating generative AI chat and tools.
Custom AI tools can be promoted as free or essential resources to build links, increase brand visibility, and generate leads/sales.
Observing how AI chatbots conduct web searches and learning how each search system works can provide insight into how to optimize content for both search engines and AI.
Screenshot from Perplexity, December 2023
Especially since Bing AI is changing how search works, Perplexity strives to modernize PageRank for better answers, and Cohere aims to improve search results with rerank technology.
Screenshot from Cohere, December 2023
Important Disclaimers About Using AI
With any AI tool or online service, it’s important to remember not to share confidential or sensitive information.
Humans may review the information you share for quality assurance purposes.
Information you share may be included in training data for future models. Some AI platforms allow you to opt out of training data in your user profiles, account settings, developer profiles, AI project settings, chat settings, etc.
Because AI tools use information scraped from the internet as part of the training data, AI-generated responses may not be accurate or unique.
Always fact-check generative AI content.
Never trust generative AI content for medical, legal, or similar advice.
Know what protections (if any) are available from your AI tools for users, subscribers, businesses, enterprises, and developers accused of copyright infringement.
Some AI tools are available for research or preview only. The content generated by them is not intended for publishing, resale, or commercial use.
While never enjoyable, it’s important to review the terms of service, content use policies, and other usage policies for tools you use, especially when you rely on them for personal or professional tasks.
The Top AI Chatbots
AI chatbots have come a long way since OpenAI released the ChatGPT “research preview” in November 2022.
Many can help you with analysis, content creation, coding, documents, images, research, summarization, and other complex tasks.
Best of all, a few – Bard, Copilot, and Pi – are free.
ChatGPT, Claude, Perplexity, and Poe offer free plans and premium subscriptions starting at $20 per month.
ChatGPT gives subscribers access to GPT-4, DALL·E, file uploads, beta features, web browsing with Bing, over 1,000 third-party plugins, the ability to create custom chatbots (GPTs), and soon, a store full of GPTs.
Claude by Anthropic offers a larger context window than most, making it better for tasks like summarizing large chunks of text and analyzing uploaded documents. Subscribers get access to the latest model and beta features.
Google Bard (Gemini Pro) connects to Google Docs, Google Drive, Gmail, YouTube, Flights, Hotels, and Maps.
Microsoft Copilot (GPT-4) uses the web and plugins from Instacart, Kayak, Klarna, and Shop.
Perplexity provides the best answers to questions with deep search and sources. Open-source models from Meta and Mistral AI are available in Labs. Subscribers can switch between GPT-4, Claude, Gemini Pro, and online LLMs.
Pi (Inflection-1) distinguishes itself as a more emotionally intelligent and personalized chat.
Poe hosts 27 official chatbots powered by the latest models from Anthropic, Google, Meta, Mistral AI, OpenAI, and Stability AI. It also allows you to create custom chatbots on many of those platforms. Subscribers get more access to the latest models.
Screenshot from Poe, December 2023
Learn Prompt Engineering
Learn how to get the most out of AI chatbots with the following free courses and guides:
Six Prompt Engineering Strategies For Better Results (OpenAI)
Best Prompting Tips For Text & Image Generation Bots (Poe)
AI Chatbots On Social Media
While Bard may have access to YouTube content, other social networks built AI chatbots into the platforms.
Meta launched a Meta AI chatbot that can generate images and search the web with Bing on Messenger, Instagram, and WhatsApp.
Quora integrates the ChatGPT bot from its AI platform, Poe, into some answers.
Snapchat’s My AI was one of the first AI chatbots that users could direct messages and integrate into group chats.
X, formerly known as Twitter, launched Grok for Premium+ subscribers, which has access to X posts/tweets as “sources” for responses.
Bytedance was working on an AI model, but the parent company of TikTok allegedly did it in a way that violated OpenAI and Microsoft TOS. This may delay the platform’s AI plans and affect the future availability of the CapCut plugin for ChatGPT.
While not a social media platform, you can also chat with Pi by Inflection on Messenger, Instagram, and WhatsApp.
AI Chatbots With Context
As you start to use AI chatbots more frequently, you may find yourself repeating certain details to get better responses.
Some platforms give you the option to add basic details about yourself, your work, and what you are trying to accomplish so that AI provides the best response for your needs.
ChatGPT lets users give context and specific directions with Custom Instructions.
Perplexity lets users offer context, specific directions, location, and language preferences in the AI Profile.
Custom Chatbots With Minimal Coding
When you need an AI chatbot that is more tailored to your needs, you can create custom chatbots in under ten minutes.
ChatGPT allows subscribers to create GPTs – chatbots that use GPT-4 with web browsing, DALL·E, and code analysis with custom instructions, knowledge files, and actions to complete specific tasks.
Microsoft Copilot Studio allows 365 users to create custom Copilot experiences.
Poe users create custom chatbots using eight of the most popular AI models. You can give each chatbot specific instructions to follow for each conversation and knowledge files to reference.
SEO Benefits Of Creating Custom AI Chatbots And GPTs
Even if you don’t need a custom AI chatbot for yourself, think about it from an SEO perspective.
Making a free tool or inexpensive solution for you or your clients that can be promoted on a webpage = links, brand visibility, and a new tool for lead generation and sales.
You can also create a new product/income stream for your clients.
Learn How To Build Custom AI Chatbots
Learn how to build custom chatbots and GPTs with these free courses and guides:
Quickstart Guide to Building Generative AI Copilots (Microsoft)
Built-In AI Chatbots And Features
You don’t always have to find a new tool to get AI assistance. Many popular business tools, software, and online services have built-in AI chatbots and features.
In addition to having AI with direct access to your company’s data, you have the confidence that comes with a platform you already trust for reliability and security.
The following are examples of platforms with scalable AI features for advertisers, marketers, agencies, teams, and enterprises.
With Zapier (and plans starting at $20 a month), you can connect your ChatGPT account, Claude (via API), or Cohere (via API) to thousands of popular business applications to create automated workflows.
For example, I can create a workflow that connects Gmail, ChatGPT, and Slack and works when a new email matching a search is received.
Screenshot from Zapier, December 2023
You can create advanced automated workflows that use your ChatGPT account or your OpenAI account with the Assistants API.
Actions For GPTs
Remember the custom AI chatbots mentioned earlier that ChatGPT subscribers can build with minimal coding?
If you’re willing to do a small amount of coding, Zapier also offers actions that connect GPTs to Zapier’s platform of app integrations.
Train AI Models And Build Generative AI Applications
If none of the AI chatbots and tools mentioned thus far provide the customized level of solution you need, then you may want to work with the following for enterprise AI solutions.
A common misconception is that AI is going to replace people at their jobs.
The reality is that AI could do most of the work that companies have outsourced for decades.
It’s the people who are willing to use AI to optimize workflows and increase productivity who will replace those who can’t or won’t adapt to the future of work with AI.
