LLMs That Code: Why Marketers Should Care (Even If You’ve Never Touched An IDE) via @sejournal, @siliconvallaeys

Large language models (LLMs) like ChatGPT and Claude are best known for their writing abilities, drafting ad copy, summarizing reports, and helping brainstorm blog content.

However, most marketers still know little about one of their most powerful features: They can write actual code.

First, it talked. Then, it wrote. Now, it builds.

We’re not just talking about basic snippets. These models can generate full scripts, fully functional browser extensions, small web apps, and automations, all from plain English prompts – or any other language you’re most comfortable with.

For marketers and PPC pros, that unlocks a new level of efficiency. You no longer need to know how to program to start benefiting from technical solutions to everyday problems.

In the past, I might have only written a script if it saved me hours of manual work every month.

Now, with LLMs, it’s so quick to build something that I’ll even create one-off tools for tasks that would’ve only taken me an hour or two. That’s how low the barrier has become.

In this article, I’ll walk through the types of problems you can solve with LLM-powered coding, the browser-based tools that make it accessible, and real examples of how marketers are already using this to move faster.

The Real Power: Turning Instructions Into Code

LLMs have ingested much of the world’s knowledge, and that includes scripts and computer code. That means if you can explain a process clearly, they can usually turn that explanation into working code.

Because they’re multi-modal, they can even understand a diagram you’ve whiteboarded at the office and turn that into code, too.

This makes them incredibly valuable for non-programmers who know what they want but don’t know how to build it.

Think of the marketer who understands how data should be formatted for a monthly client report but dreads the repetitive steps of reformatting CSV files. Or the account manager who wants to automate their process of eliminating underperforming search terms but doesn’t have a dev team to help them.

With LLMs, these tasks can be described in a few sentences, and AI can generate Python scripts, JavaScript tools, or even complete web apps that solve the problem.

This isn’t just about saving time. It’s about unlocking experimentation and removing the friction that keeps good ideas from scaling through technology.

What Problems Can LLM-Generated Code Solve?

Let’s break down the kinds of problems where LLM coding can shine. These aren’t hypothetical; they’re pulled from common workflows across agencies and in-house teams daily.

1. Automating Repetitive, Time-Consuming Tasks

You probably do at least one of these on a somewhat regular basis:

• Reformatting exported Sheets or CSV files.
• Copying Google Ads data into slides for reporting.
• Cleaning up GPT’s output before sharing it with a client.
• Manually reviewing ad copy for brand compliance.

With the help of an LLM, each of these can be turned into a repeatable, automatable workflow. You describe the task, and the LLM builds the script that does it.

This is especially valuable for marketers who are tired of being “spreadsheet operators” instead of strategists. By turning routine tasks into one-click tools, you free up hours a week and reduce human error.

2. Trying Something Entirely New

Unlike the tasks above, which you know exactly how to do but hate how much time they take, there are also some projects you may not have tried because you do not know how.

For my team, that included a quiz to make blog content more engaging. For me, it involved building a browser extension to blur sensitive data on the screen.

These are ideal use cases for LLM-powered coding. They allow you to prototype and test ideas without needing a development team, and if you’re lucky enough to have one, you don’t need to wait for your project to get prioritized.

You can get feedback quickly, iterate faster, and build an entire proof of concept before involving engineering.

Marketing innovation often dies in the backlog. LLM coding makes it easier to try things on your own.

3. Google Ads Scripts

This is one of the most exciting areas for PPC pros. Google Ads scripts are powerful, but let’s face it, they’ve always had a learning curve. Now, LLMs can flatten that curve dramatically.

You can tell a model:

Write a Google Ads script that checks all active campaigns with “Mother’s Day” in the name. If the current date is within seven days of Mother’s Day, increase the daily budget of those campaigns by 20%. Include comments to explain each part of the code so I can understand what the script is doing.

It will return a fully functional script that you can paste directly into your account’s scripts section.

This lowers the barrier to entry for marketers who want to automate common PPC maintenance or build lightweight tools for managing large accounts.

You can go from idea to automation in minutes, no JavaScript experience required.

Tools That Make LLM Coding Accessible

I hope the idea of becoming more efficient through code sparks your interest, especially if you’ve ever found yourself repeating the same task week after week.

Whether you’re managing ad campaigns, cleaning data, or formatting content, the ability to automate even small pieces of your workflow can save hours and reduce errors.

Here’s the best part: You don’t need to be a developer to start.

You don’t even need to install anything, understand programming languages, or know how to set up a server. You definitely don’t need to open a complicated integrated development environment (IDE).

The tools I’m about to show you run entirely in your browser. They’re designed to help you go from idea to functional code with nothing more than a clear description of what you want to achieve.

If you’ve never written code before, this is exactly where you want to start.

Claude (Anthropic)

For marketers, Claude’s ability to write, test, and execute code right inside the interface is a real standout.

No setup is required, no installations, and no APIs to connect.

You describe what you need, Claude writes the code, and you see the results in real-time. This fast, feedback-driven loop makes it easier than ever to experiment and iterate without the usual technical friction.

The 200,000-token context window is another game-changer. You can paste your entire campaign structure, a long analytics report, or even a full landing page copy, and Claude will process it all in one go.

It keeps track of every detail you’ve shared, so nothing gets lost as you build on your ideas.

There is a tradeoff, though. Claude currently runs code in a single-file execution environment. That’s fine for most marketing tasks, but for more complex, multi-file projects, it’s not as flexible as tools like Vercel’s V0.dev, which supports full project structures.

Still, for marketers building scrappy, high-impact tools fast, Claude handles a surprising amount.

Here’s what’s most exciting to me:

• It can run JavaScript right in the browser, perfect for quick tasks like data filtering, simple visualizations, or interactive prototypes.
• It translates technical concepts into plain, marketing-friendly language, so you’re never stuck decoding dev speak.
• It surfaces insights from your data quickly, helping you spot trends and outliers that would otherwise go unnoticed.

One of the benefits of LLMs is that they can adapt to each user’s level.

If you’re not technical, it gives you just enough to feel empowered. If you are, it meets you there and helps you move even faster. Either way, it expands what’s possible without getting in your way.

Below is a view of Claude generating code based on a marketing-focused prompt, with both the prompt and working output visible in the interface.

Users can toggle to the code view if they prefer to see that instead of a preview of the tool.

Screenshot from Claude.ai, April 2025

V0.dev (Vercel)

As much as I’m excited about Claude, V0 takes it to a whole new level.

Vercel, the creator of Next.js, made V0.dev, which is designed to build working software by describing what you need.

Why it stands out:

• Generates full React components, HTML, and CSS.
• Lets you deploy working projects instantly.
• Handles multi-file architecture (great for real apps).

Marketers can use V0.dev to build:

• Text reformatting tools.
• User interfaces (UI/UX).
• Internal dashboards.
• Fully working web apps.

It’s like having a front-end developer in your browser.

Here’s a screenshot of what I quickly tried building using V0.dev. I prompted it to create a simple tool for Search Engine Journal readers that takes a blog post and outputs key takeaways in bullet form.

V0.dev generated a clean, on-brand interface with just a single prompt, no coding required. It’s a great example of how fast you can go from idea to working prototype.

What’s especially cool is that you could even launch this tool so anyone can use it.

Screenshot from v0.dev, April 2025

When creating a tool that requires third-party integrations, V0 asks for the required API keys and credentials.

When building something that can’t be hosted online, like a Chrome extension, it explains how to install the files. In short, it helps anyone, regardless of ability, to create a working piece of software.

GPT-4o (OpenAI/ChatGPT)

GPT-4o is the LLM I’ve used the most for building ad scripts, as it was the first one to write an error-free piece of code. It’s also great for:

• Creating data transformation scripts.
• Debugging code.
• Explaining errors.
• Translating code from one language to another.

But, GPT is limited in that it can’t run the code it writes directly in the chat window. That means there is a lot of copy-and-pasting required to take the code, install it on a server, test it, and then iterate with GPT to debug it.

While I think GPT is awesome for writing code, if I need something quick and simple, I’ll prefer Claude. If I want something more complex and want to debug it in the LLM, I’ll use V0.

Real-World Example Use Cases

Let’s go deeper into actual examples. These aren’t just ideas; these are projects you can ask an LLM to help build today.

Example 1: Chrome Extension To Blur Sensitive Text

The Problem:

I’m frequently taking screenshots of dashboards or search results but need to hide client names, numbers, or other sensitive data.

The LLM Solution:

I asked V0.dev to generate a Chrome extension that adds a blur effect to any numerical values on the page.

It generates all the files needed and explains how to install my custom extension in my Chrome browser. It returns:

• The manifest.json file.
• JavaScript to inject CSS.
• Instructions to package and install the extension.

Screenshot from Optmyzr.com, April 2025

Why It Matters:

This isn’t something most marketers would ever think to build, but with a few prompts, you’ve created a privacy-preserving utility that saves you editing time and protects sensitive info.

Example 2: Web App To Reformat GPT Output

The Problem:

I use Deep Research from ChatGPT to generate research for my team or future blogs, but I don’t love how source references are formatted when I copy the research into a Google Doc.

The LLM Solution:

Use V0.dev to create a web app that:

• Accepts pasted text.
• Accepts a list of formatting changes I would normally make manually (e.g., finding links and putting them in superscript).
• Displays the cleaned version instantly.

Why It Matters:

It streamlines content workflows. Instead of editing output by hand, you get consistent formatting that meets your brand or platform guidelines.

Example 3: Interactive Blog Quiz Generator

The Problem:

We wanted to make our blogs more interactive, and my team had the idea to add quizzes.

The LLM Solution:

Use Claude to generate a quiz engine in HTML/CSS/JS. Feed it five to seven questions, then tie the result to different calls to action (“Download This Guide” or “Talk to an Expert”).

Why It Matters:

Interactive content improves time-on-page, reduces bounce, and personalizes the experience, without needing design or dev support.

Want to see it? Check out how AI is transforming our content about bidding strategies.

Screenshot from Optmyzr Blog, April 2025

Conclusion: Marketers Can Now Build What They Need With AI

Writing utility software is easier than it’s ever been before.

For marketers, the question used to be “What tools should I use?” Now, it might be: “What tools should I create?”

If you’ve ever been bottlenecked by engineering resources, or if your “wouldn’t it be cool if…” idea has sat in a notebook for months, this is your chance.

You don’t need an IDE. You don’t need to understand loops or classes. You just need a problem to solve, a clear description, and the right LLM at your side.

More Resources:

Featured Image: Thantaree/Shutterstock

How I Edit AI Content: A Workflow For The New Age Of Content Creation via @sejournal, @Kevin_Indig

In last week’s Memo, I explained how, just as digital DJing transformed music mixing, AI is revolutionizing how we create content by giving us instant access to diverse expressions and ideas.

Instead of fighting this change, writers should embrace AI as a starting point while focusing our energy on adding uniquely human elements that machines can’t replicate, like our personal experiences, moral judgment, and cultural understanding.

Last week, I identified seven distinctly human writing capabilities and 11 telltale signs of AI-generated content.

Today, I want to show you how I personally apply these insights in my editing process.

Image Credit: Lyna ™

Rather than seeing AI as my replacement, I advocate for thoughtful collaboration between human creativity and AI efficiency, much like how skilled DJs don’t just play songs but transform them through artistic mixing.

As someone who’s spent countless hours editing and tinkering with AI-generated drafts, I’ve noticed most people get stuck on grammar fixes while missing what truly makes writing connect with readers.

They overlook deeper considerations like:

• Purposeful imperfection: Truly human writing isn’t perfectly polished. Natural quirks, occasional tangents, and varied sentence structures create authenticity that perfect grammar and flawless organization can’t replicate.
• Emotional intelligence: AI content often lacks the intuitive emotional resonance that comes from lived experience. Editors frequently correct grammar but overlook opportunities to infuse genuine emotional depth.
• Cultural context: Humans naturally reference shared cultural touchpoints and adapt their tone based on context. This awareness is difficult to edit into AI content without completely reframing passages.

In today’s Memo, I explain how to turn these edits into a recurring workflow for you or your team, so you can leverage the power of AI, accelerate content output, and drive more organic revenue.

Boost your skills with Growth Memo’s weekly expert insights. Subscribe for free!

Turning AI-Editing Into A Workflow

Image Credit: Kevin Indig

I like to edit AI content in several passes, each with a specific focus:

• Round 1: Structure.
• Round 2: Language.
• Round 3: Humanization.
• Round 4: Polish.

Not every type of content needs the same amount of editing:

• You can be more hands-off with supporting content on category or product pages, while editorial content for blogs or content hubs needs significantly more editing.
• In the same way, evergreen topics need less editing while thought leadership needs a heavy editorial hand.

Round 1: Structure & Big-Picture Review

First, I read the entire draft like a skeptical reader would.

I’m looking for logical flow issues, redundant sections, and places where the AI went on unhelpful tangents.

This is about getting the bones right before polishing sentences.

Rather than nitpicking grammar, I ask: “Does this piece make sense? Would a human actually structure it this way?”

But, the most important question is: “Does this piece meet user intent?” You need to ensure that the structure optimizes for speed-to-insights and helps users solve the implied problem of their searches.

If sections feel out of order or disconnected, I rearrange them.

If the AI repeats the same point in multiple places (they love doing this), I consolidate.

Round 2: Humanize The Language & Flow

Next, I tackle that sterile AI tone that makes readers’ eyes glaze over.

I break up the robotic rhythm by:

• Consciously varying sentence lengths (Watch this. I’m doing it right now. Different lengths create natural cadence.).
• Replacing corporate-speak with how humans actually talk (“use” instead of “utilize,” “start” instead of “commence”).
• Cutting those meaningless filler sentences AI loves to add (“It’s important to note that…” or “As we can see from the above…”).

For example, I’d transform this AI-written line:

Utilizing appropriate methodologies can facilitate enhanced engagement among target demographics.

Into this:

Use the right approach, and people will actually care about what you’re saying.

Round 3: Add The Human Value Only You Can Provide

Here’s where I earn my keep.

I infuse the piece with:

• Opinions where appropriate.
• Personal stories or examples.
• Unique metaphors or cultural references.
• Nuanced insights that come from my expertise.

One of the shifts we have to make – and that I made – is to be more deliberate about collecting stories and opinions that we can tell.

In his book “Storyworthy,” Matthew Dicks shares how he saves stories from everyday life in a spreadsheet. He calls this habit Homework For Life, and it’s the most effective way to collect relatable stories that you can use for your content. It’s also a way to slow down time:

As you begin to take stock of your days, find those moments — see them and record them — time will begin to slow down for you. The pace of your life will relax.

Round 4: Final Polish & Optimization

Finally, I do a last pass focusing on:

• A punchy opening that hooks the reader.
• Removing any lingering AI patterns (overly formal language, repetitive phrases).
• Search optimization (user intent, headings, keywords, internal links) without sacrificing readability.
• Fact-checking every statistic, date, name, and claim.
• Adding calls to action or questions that engage readers.

I know I’ve succeeded when I read the final piece and genuinely forget that an AI was involved in the drafting process.

The ultimate test: “Would I be proud to put my name on this?”

AI Content Editing Checklist

Before you hit “Publish,” run through this checklist to make sure you’ve covered all bases:

• ✅ User Intent: The content is organized logically and addresses the intended topic or keyword completely, without off-topic detours.
• ✅ Tone & Voice: The writing sounds consistently human and aligns with brand voice (e.g., friendly, professional, witty, etc.).
• ✅ Readability: Sentences and paragraphs are concise and easy to read. Jargon is explained or simplified. The formatting (headings, lists, etc.) makes it skimmable.
• ✅ Repetition: No overly repetitive phrases or ideas. Redundant content is trimmed. The language is varied and interesting.
• ✅ Accurate: All facts, stats, names, and claims have been verified. Any errors are corrected. Sources are cited for important or non-obvious facts, lending credibility. There are no unsupported claims or outdated information.
• ✅ Original Value: The content contains unique elements (experiences, insights, examples, opinions) that did not come from AI.
• ✅ SEO: The primary keyword and relevant terms are included naturally. Title and headings are optimized and clear. Internal links to related content are added where relevant. External links to authoritative sources support the content.
• ✅ Polish: The introduction is compelling. The content includes elements that engage the reader (questions, conversational bits) and a call to action. It’s free of typos and grammatical errors. All sentences flow well.

If you can check off all (or most) of these items, you’ve likely turned the AI draft into a high-quality piece that can confidently be published.

AI Content Editing = Remixing

We’ve come full circle.

Just as digital technology transformed DJing without eliminating the need for human creativity and curation, AI is reshaping writing while still requiring our uniquely human touch.

The irony I mentioned at the start of this article – trying to make AI content more human – becomes less ironic when we view AI as a collaborative tool rather than a replacement for human creativity.

Just as DJs evolved from vinyl crates to digital platforms without losing their artistic touch, writers are adapting to use AI while maintaining their unique value.

You can raise the chances of creating high-performing content that stands out by selecting the right models, inputs, and direction:

• The newest models lead to exponentially better content than older (cheaper) ones. Don’t try to save money here.
• Spend a lot of time getting style guides and examples right so the models work in the right lanes.
• The more unique your data sources are, the more defensible your AI draft becomes.

The key insight is this: AI content editing is about enhancing the output with the irreplaceable human elements that make content truly engaging.

Whether that’s through adding lived experience, cultural understanding, emotional depth, or purposeful imperfection, our role is to be the bridge between AI’s computational efficiency and human connection.

The future belongs not to those who resist AI but to those who learn to dance with it, knowing exactly when to lead with their uniquely human perspective and when to follow the algorithmic beat.

Back in my DJ days, the best sets weren’t about the equipment I used but about the moments of unexpected connection I created.

The same holds true for writing in this new era.

Featured Image: Paulo Bobita/Search Engine Journal

How To Get Brand Mentions In Generative AI via @sejournal, @AlliBerry3

There’s been a lot of talk recently about whether large language models (LLMs) are replacing a considerable amount of Google searches.

While Google is clearly still the market leader, with 14 billion searches per day worldwide, an estimated 37.5 million “searches” on ChatGPT represent an opportunity for your brand.

SEO professionals have years of experience testing optimization tactics on Google, but we’re still at the beginning stages of understanding how to get your brand cited in generative AI chatbots.

This is an exciting opportunity because it forces people to test and learn rapidly.

Through some testing and research, here are some tips that have helped me develop initial recommendations for my clients for generative AI optimization, regardless of whether it’s ChatGPT, Gemini, Deepseek, or whatever generative AI chatbot comes next.

Use Generative AI Chatbots To Learn About Your Brand

First, use generative AI tools and start asking them questions about your brand to find the sources they utilize to answer your queries.

This will help you better understand what sources it’s been trained on and what pages on your site (or competitor sites) matter to its understanding of your brand.

Ask questions like:

• Tell me about [company]/[product].
• What are the best brands in [vertical] and why?
• What are the pros and cons of [company]/[product]?

For example, when I ask ChatGPT-4o, “Tell me about HubSpot,” it gives me a nice summary with a lot of useful citations:

Screenshot from ChatGPT, April 2025

From this, you can see that a legal page is being cited multiple times in a company overview, so those are important. You can also see the HubSpot Knowledge Base where information is being pulled from as well.

Often, a company’s About page is the main citation, but clearly, HubSpot has built out a better legal section than its core pages.

If I were part of its organization, I would work to make the About page richer with information. Generally, your About page will do better at marketing the benefits of your products than legal pages.

When I then asked, “What are the best brands for small business marketing?”, it provided me with the following list:

Screenshot from ChatGPT, April 2025

Screenshot from ChatGPT, April 2025

ChatGPT-4o cites Wikipedia five different times and NerdWallet once for its affiliate coverage of small business marketing tools.

In searches I’ve done in other sectors, I’ve seen a lot more variety in sources listed – many in the affiliate review space. Here, however, NerdWallet is the only one.

When I asked ChatGPT-4o to dive into HubSpot further and show me the pros and cons of using it for small business marketing, it responded with:

Screenshot from ChatGPT, April 2025

Screenshot from ChatGPT, April 2025

I would then take this list and compare it against how I market the product to small business owners and potentially make tweaks accordingly.

And if there is validity to the cons listed and they are weaknesses we want to work on as an organization, I would start to build relationships with some of the sources listed.

That way, when there are company updates that impact some of what’s been written about the company, they can update their review pages, and it’ll impact what shows up in LLM queries.

I would also engage with the PR team about getting more coverage for the brand. Some of these citations are not particularly well-known or credible sites, so there is opportunity to get more authoritative sources to show up.

Ensure LLMs Can Crawl Your Website

This was true at the beginning of SEO, and is still true now.

Ensure you have a robots.txt file on your website’s server with directives to crawlers about pages and sections they can crawl and index.

A lot of site owners initially rushed to block LLMs from crawling their sites when ChatGPT first launched, as it was unknown (and also probably scraping content for the model).

If you want to be included in generative AI results now, though, you need to be where the AI crawlers can see you, so double-check that it is all configured correctly.

Utilize Credible Citations And Quotes In Content

A group of researchers from several prominent universities conducted a study on AI search engine optimization and what was more likely to surface in response to queries.

The tactic that worked the best, especially for factual queries, was adding citations from authoritative sources.

Using language like “according to [source],” adding a statistic with a credible citation, or a quote from a known expert all increased the likelihood of showing up in a generative AI chatbot responses by as much as 25.1% for sites ranking in position 4 in Google and by 99.7% for sites ranking in position 5 in Google.

Similarly, adding statistics to content led to a 10% increase in visibility in LLMs if the site is in position 4 in Google and a 97% increase in visibility in LLMs if the site is ranked in position 5 in Google.

Mentions In Prominent Databases And Forums Help

There are lots of reasons to be paying attention to prominent forums like Reddit and Quora or popular database sites like Wikipedia. Not only do they own lots of organic search real estate, but they are also obvious sites for training LLMs.

Reddit is now, smartly, selling data licensing to AI companies. Being a topic of discussion on these sites will only help your brand. There’s no better time to get into being active on Reddit than now.

Engaging authentically on behalf of a brand (assuming you reveal your affiliation upfront) is more acceptable nowadays and is often welcomed to get clarification on user questions. It will likely benefit you on your generative AI optimization journey, too.

Develop An Exceptional About Page

If there is one area of your website you need to improve on, your About page may be it.

Generative AI models utilize these types of pages to understand what a company does and how credible the company is.

If you ask any of these platforms for information about your brand, you may be surprised by how heavily they rely on your About page to deliver the answer.

If your About page doesn’t describe your business and products well enough, you may see LLMs citing legal pages instead, like in the case of HubSpot mentioned earlier.

Focus On Long-Tail Keywords

Modern transformer-based LLMs are based on a statistical analysis of the co-occurrence of words.

If an entity is mentioned in connection with another entity with frequency in the training data, there is a high probability of a semantic relationship between the two entities.

To optimize for this, it’s more useful to use keyword research tools to better understand related keywords.

Search volume can still be an indicator of importance, but I would focus more on better understanding the relationships and relevance between concepts, ensuring the content is of high quality, and that user intent is matched.

Stop Siloing SEO

We’re entering an era when websites get fewer and fewer clicks from organic search. For most brands, a multi-channel strategy has never been more imperative.

Not only does building brand recognition help fuel some of the other best practices here, but LLMs are also being trained on social media and marketing content.

Having an aligned, cross-channel strategy only strengthens your brand.

Plus, the more you can build a sales flywheel in your own content ecosystem, the less you need to panic about staying ahead of the ever-evolving world of SEO.

Track Your Referrals And Reverse-Engineer

Once you start seeing generative AI platforms driving traffic to your site, start paying attention to what pages bring that traffic in.

Then, visit that generative AI platform and try to recreate searches that could lead to your page as the answer.

You’ll start to learn what topics these platforms associate with your brand, and then you can find ways to double down on that type of content.

Final Thoughts

While the tool companies are trying to catch up with how to help digital marketers optimize in this era of generative AI, we will have to be more reliant on ourselves to reverse-engineer what we’re seeing in the data and run our own experiments.

More Resources:

Featured Image: Visual Generation/Shutterstock