Google has published guidelines on what to do if your rankings are affected after being incorrectly flagged by Google’s SafeSearch filter. The new documentation offers three actions to take to resolve the issues.
The new documentation provides guidance on three steps to take:
How to check if Google’s Safe Search is filtering out a website.
Guide to how to fix common mistakes
Troubleshooting steps
SafeSearch Filtering
Google’s SafeSearch is a filtering system that removes explicit content from the search results. But there may be times when it fails and mistakenly removes the wrong content.
These are Google’s official steps for verifying if a site is being filtered:
“Confirm that SafeSearch is set to Off.
Search for a term where you can find that page in search results.
Set SafeSearch to Filter. If you don’t see your page in the results anymore, it is likely being affected by SafeSearch filtering on this query.”
To check if the entire site is being filtered by SafeSearch, Google recommends doing a site: search for your domain, then set the SafeSearch setting to “Filter” and if the site doesn’t appear in a site: search that means that Google is filtering out the entire website.
If mistakes were found and fixed it takes Google at least two to three months for the algorithmic classifiers to clear the site. Only after three months have passed does Google recommend requesting a manual review.
Read Google’s guidance on recovering a site from incorrect flagging:
The WordPress Performance Team has released an experimental plugin that increases the perceived loading speed of web pages without the performance issues and accessibility tradeoffs associated with Single Page Applications (SPAs). The announcement was made by Felix Arntz, a member of the WordPress Performance Team and a Google software engineer.
Plugins released by the WordPress Performance Team are released so that users can play around and test with a new performance enhancement before the new feature is considered for inclusion into the WordPress core. Using these plugins provides a way to receive advanced performance improvements before a decision is made as to whether to integrate the improvements into WordPress itself.
The View Transitions plugin brings smooth, native browser-powered animations to WordPress page loads, mimicking the feel of Single Page Applications (SPAs) without requiring a full rebuild or custom JavaScript. Once the WordPress plugin is activated, it replaces the default hard reload between pages with a fluid animated transition effect, like a fade or slide, depending on how you configure it. This improves the visual flow of navigation across the site and increases the perceived loading speed for site visitors.
The plugin works out of the box with most themes, and users can customize the behavior through the admin user interface under Settings > Reading. Animations can be set using selectors and presets, with support for things like headers, post titles, and featured images to persist or animate across views.
According to the announcement:
“You can customize the default animation, and the selectors for the default view transition names for both global and post-specific elements. While this means the customization options are limited via the UI, it still allows you to play around with different configurations via UI, and likely for the majority of sites these are the most relevant parameters to customize anyways.
Keep in mind that this UI is only supplemental, and it only exists for easy exploration in the plugin. The recommended way to customize is via add_theme_support in your site’s WordPress theme.
…For the default-animation, a few animations are available by default. Additionally, the plugin provides an API to register additional animations, each of which encompasses a unique identifier, some configuration values, a CSS stylesheet, and optional aliases.”
The new WordPress plugin is optimized for block themes but designed to work broadly across all WordPress sites.
The page transitions are supported by all modern browsers, however it will degrade gracefully in older unsupported browsers by falling back to standard navigation without breaking anything.
The main point is that the plugin makes WordPress sites feel more modern and app-like—without the complexity or downsides of SPAs.
MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.
The way DARPA tells it, math is stuck in the past. In April, the US Defense Advanced Research Projects Agency kicked off a new initiative called expMath—short for Exponentiating Mathematics—that it hopes will speed up the rate of progress in a field of research that underpins a wide range of crucial real-world applications, from computer science to medicine to national security.
“Math is the source of huge impact, but it’s done more or less as it’s been done for centuries—by people standing at chalkboards,” DARPA program manager Patrick Shafto said in a video introducing the initiative.
The modern world is built on mathematics. Math lets us model complex systems such as the way air flows around an aircraft, the way financial markets fluctuate, and the way blood flows through the heart. And breakthroughs in advanced mathematics can unlock new technologies such as cryptography, which is essential for private messaging and online banking, and data compression, which lets us shoot images and video across the internet.
But advances in math can be years in the making. DARPA wants to speed things up. The goal for expMath is to encourage mathematicians and artificial-intelligence researchers to develop what DARPA calls an AI coauthor, a tool that might break large, complex math problems into smaller, simpler ones that are easier to grasp and—so the thinking goes—quicker to solve.
Mathematicians have used computers for decades, to speed up calculations or check whether certain mathematical statements are true. The new vision is that AI might help them crack problems that were previously uncrackable.
But there’s a huge difference between AI that can solve the kinds of problems set in high school—math that the latest generation of models has already mastered—and AI that could (in theory) solve the kinds of problems that professional mathematicians spend careers chipping away at.
On one side are tools that might be able to automate certain tasks that math grads are employed to do; on the other are tools that might be able to push human knowledge beyond its existing limits.
Here are three ways to think about that gulf.
1/ AI needs more than just clever tricks
Large language models are not known to be good at math. They make things up and can be persuaded that 2 + 2 = 5. But newer versions of this tech, especially so-called large reasoning models (LRMs) like OpenAI’s o3 and Anthropic’s Claude 4 Thinking, are far more capable—and that’s got mathematicians excited.
This year, a number of LRMs, which try to solve a problem step by step rather than spit out the first result that comes to them, have achieved high scores on the American Invitational Mathematics Examination (AIME), a test given to the top 5% of US high school math students.
At the same time, a handful of new hybrid models that combine LLMs with some kind of fact-checking system have also made breakthroughs. Emily de Oliveira Santos, a mathematician at the University of São Paulo, Brazil, points to Google DeepMind’s AlphaProof, a system that combines an LLM with DeepMind’s game-playing model AlphaZero, as one key milestone. Last year AlphaProof became the first computer program to match the performance of a silver medallist at the International Math Olympiad, one of the most prestigious mathematics competitions in the world.
The uptick in progress is clear. “GPT-4 couldn’t do math much beyond undergraduate level,” says de Oliveira Santos. “I remember testing it at the time of its release with a problem in topology, and it just couldn’t write more than a few lines without getting completely lost.” But when she gave the same problem to OpenAI’s o1, an LRM released in January, it nailed it.
Does this mean such models are all set to become the kind of coauthor DARPA hopes for? Not necessarily, she says: “Math Olympiad problems often involve being able to carry out clever tricks, whereas research problems are much more explorative and often have many, many more moving pieces.” Success at one type of problem-solving may not carry over to another.
Others agree. Martin Bridson, a mathematician at the University of Oxford, thinks the Math Olympiad result is a great achievement. “On the other hand, I don’t find it mind-blowing,” he says. “It’s not a change of paradigm in the sense that ‘Wow, I thought machines would never be able to do that.’ I expected machines to be able to do that.”
That’s because even though the problems in the Math Olympiad—and similar high school or undergraduate tests like AIME—are hard, there’s a pattern to a lot of them. “We have training camps to train high school kids to do them,” says Bridson. “And if you can train a large number of people to do those problems, why shouldn’t you be able to train a machine to do them?”
Sergei Gukov, a mathematician at the California Institute of Technology who coaches Math Olympiad teams, points out that the style of question does not change too much between competitions. New problems are set each year, but they can be solved with the same old tricks.
“Sure, the specific problems didn’t appear before,” says Gukov. “But they’re very close—just a step away from zillions of things you have already seen. You immediately realize, ‘Oh my gosh, there are so many similarities—I’m going to apply the same tactic.’” As hard as competition-level math is, kids and machines alike can be taught how to beat it.
That’s not true for most unsolved math problems. Bridson is president of the Clay Mathematics Institute, a nonprofit US-based research organization best known for setting up the Millenium Prize Problems in 2000—seven of the most important unsolved problems in mathematics, with a $1 million prize to be awarded to the first person to solve each of them. (One problem, the Poincaré conjecture, was solved in 2010; the others, which include P versus NP and the Riemann hypothesis, remain open). “We’re very far away from AI being able to say anything serious about any of those problems,” says Bridson.
And yet it’s hard to know exactly how far away, because many of the existing benchmarks used to evaluate progress are maxed out. The best new models already outperform most humans on tests like AIME.
To get a better idea of what existing systems can and cannot do, a startup called Epoch AI has created a new test called FrontierMath, released in December. Instead of co-opting math tests developed for humans, Epoch AI worked with more than 60 mathematicians around the world to come up with a set of math problems from scratch.
FrontierMath is designed to probe the limits of what today’s AI can do. None of the problems have been seen before and the majority are being kept secret to avoid contaminating training data. Each problem demands hours of work from expert mathematicians to solve—if they can solve it at all: some of the problems require specialist knowledge to tackle.
FrontierMath is set to become an industry standard. It’s not yet as popular as AIME, says de Oliveira Santos, who helped develop some of the problems: “But I expect this to not hold for much longer, since existing benchmarks are very close to being saturated.”
On AIME, the best large language models (Anthropic’s Claude 4, OpenAI’s o3 and o4-mini, Google DeepMind’s Gemini 2.5 Pro, X-AI’s Grok 3) now score around 90%. On FrontierMath, 04-mini scores 19% and Gemini 2.5 Pro scores 13%. That’s still remarkable, but there’s clear room for improvement.
FrontierMath should give the best sense yet just how fast AI is progressing at math. But there are some problems that are still too hard for computers to take on.
2/ AI needs to manage really vast sequences of steps
Squint hard enough and in some ways math problems start to look the same: to solve them you need to take a sequence of steps from start to finish. The problem is finding those steps.
“Pretty much every math problem can be formulated as path-finding,” says Gukov. What makes some problems far harder than others is the number of steps on that path. “The difference between the Riemann hypothesis and high school math is that with high school math the paths that we’re looking for are short—10 steps, 20 steps, maybe 40 in the longest case.” The steps are also repeated between problems.
“But to solve the Riemann hypothesis, we don’t have the steps, and what we’re looking for is a path that is extremely long”—maybe a million lines of computer proof, says Gukov.
Finding very long sequences of steps can be thought of as a kind of complex game. It’s what DeepMind’s AlphaZero learned to do when it mastered Go and chess. A game of Go might only involve a few hundred moves. But to win, an AI must find a winning sequence of moves among a vast number of possible sequences. Imagine a number with 100 zeros at the end, says Gukov.
But that’s still tiny compared with the number of possible sequences that could be involved in proving or disproving a very hard math problem: “A proof path with a thousand or a million moves involves a number with a thousand or a million zeros,” says Gukov.
No AI system can sift through that many possibilities. To address this, Gukov and his colleagues developed a system that shortens the length of a path by combining multiple moves into single supermoves. It’s like having boots that let you take giant strides: instead of taking 2,000 steps to walk a mile, you can now walk it in 20.
The challenge was figuring out which moves to replace with supermoves. In a series of experiments, the researchers came up with a system in which one reinforcement-learning model suggests new moves and a second model checks to see if those moves help.
They used this approach to make a breakthrough in a math problem called the Andrews-Curtis conjecture, a puzzle that has been unsolved for 60 years. It’s a problem that every professional mathematician will know, says Gukov.
(An aside for math stans only: The AC conjecture states that a particular way of describing a type of set called a trivial group can be translated into a different but equivalent description with a certain sequence of steps. Most mathematicians think the AC conjecture is false, but nobody knows how to prove that. Gukov admits himself that it is an intellectual curiosity rather than a practical problem, but an important problem for mathematicians nonetheless.)
Gukov and his colleagues didn’t solve the AC conjecture, but they found that a counterexample (suggesting that the conjecture is false) proposed 40 years ago was itself false. “It’s been a major direction of attack for 40 years,” says Gukov. With the help of AI, they showed that this direction was in fact a dead end.
“Ruling out possible counterexamples is a worthwhile thing,” says Bridson. “It can close off blind alleys, something you might spend a year of your life exploring.”
True, Gukov checked off just one piece of one esoteric puzzle. But he thinks the approach will work in any scenario where you need to find a long sequence of unknown moves, and he now plans to try it out on other problems.
“Maybe it will lead to something that will help AI in general,” he says. “Because it’s teaching reinforcement learning models to go beyond their training. To me it’s basically about thinking outside of the box—miles away, megaparsecs away.”
3/ Can AI ever provide real insight?
Thinking outside the box is exactly what mathematicians need to solve hard problems. Math is often thought to involve robotic, step-by-step procedures. But advanced math is an experimental pursuit, involving trial and error and flashes of insight.
That’s where tools like AlphaEvolve come in. Google DeepMind’s latest model asks an LLM to generate code to solve a particular math problem. A second model then evaluates the proposed solutions, picks the best, and sends them back to the LLM to be improved. After hundreds of rounds of trial and error, AlphaEvolve was able to come up with solutions to a wide range of math problems that were better than anything people had yet come up with. But it can also work as a collaborative tool: at any step, humans can share their own insight with the LLM, prompting it with specific instructions.
This kind of exploration is key to advanced mathematics. “I’m often looking for interesting phenomena and pushing myself in a certain direction,” says Geordie Williamson, a mathematician at the University of Sydney in Australia. “Like: ‘Let me look down this little alley. Oh, I found something!’”
Williamson worked with Meta on an AI tool called PatternBoost, designed to support this kind of exploration. PatternBoost can take a mathematical idea or statement and generate similar ones. “It’s like: ‘Here’s a bunch of interesting things. I don’t know what’s going on, but can you produce more interesting things like that?’” he says.
Such brainstorming is essential work in math. It’s how new ideas get conjured. Take the icosahedron, says Williamson: “It’s a beautiful example of this, which I kind of keep coming back to in my own work.” The icosahedron is a 20-sided 3D object where all the faces are triangles (think of a 20-sided die). The icosahedron is the largest of a family of exactly five such objects: there’s the tetrahedron (four sides), cube (six sides), octahedron (eight sides), and dodecahedron (12 sides).
Remarkably, the fact that there are exactly five of these objects was proved by mathematicians in ancient Greece. “At the time that this theorem was proved, the icosahedron didn’t exist,” says Williamson. “You can’t go to a quarry and find it—someone found it in their mind. And the icosahedron goes on to have a profound effect on mathematics. It’s still influencing us today in very, very profound ways.”
For Williamson, the exciting potential of tools like PatternBoost is that they might help people discover future mathematical objects like the icosahedron that go on to shape the way math is done. But we’re not there yet. “AI can contribute in a meaningful way to research-level problems,” he says. “But we’re certainly not getting inundated with new theorems at this stage.”
Ultimately, it comes down to the fact that machines still lack what you might call intuition or creative thinking. Williamson sums it up like this: We now have AI that can beat humans when it knows the rules of the game. “But it’s one thing for a computer to play Go at a superhuman level and another thing for the computer to invent the game of Go.”
“I think that applies to advanced mathematics,” he says. “Breakthroughs come from a new way of thinking about something, which is akin to finding completely new moves in a game. And I don’t really think we understand where those really brilliant moves in deep mathematics come from.”
Perhaps AI tools like AlphaEvolve and PatternBoost are best thought of as advance scouts for human intuition. They can discover new directions and point out dead ends, saving mathematicians months or years of work. But the true breakthroughs will still come from the minds of people, as has been the case for thousands of years.
For now, at least. “There’s plenty of tech companies that tell us that won’t last long,” says Williamson. “But you know—we’ll see.”
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
What’s next for AI and math
The modern world is built on mathematics. Math lets us model complex systems such as the way air flows around an aircraft, the way financial markets fluctuate, and the way blood flows through the heart. Mathematicians have used computers for decades, but the new vision is that AI might help them crack problems that were previously uncrackable.
However, there’s a huge difference between AI that can solve the kinds of problems set in high school—math that the latest generation of models has already mastered—and AI that could (in theory) solve the kinds of problems that professional mathematicians spend careers chipping away at. Here are three ways to understand that gulf.
—Will Douglas HeavenThis story is from our What’s Next series, which looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.
Inside the effort to tally AI’s energy appetite
—James O’Donnell
After working on it for months, my colleague Casey Crownhart and I finally saw our story on AI’s energy and emissions burden go live last week.
The initial goal sounded simple: Calculate how much energy is used when we interact with a chatbot, then tally that up to understand why leaders in tech and politics are so keen to harness unprecedented levels of electricity to power AI and reshape our energy grids in the process.
It was, of course, not so simple. After speaking with dozens of researchers, we realized that the common understanding of AI’s energy appetite is full of holes. I encourage you to read the full story, which has some incredible graphics to help you understand this topic. But here are three takeaways I have after the project.
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Elon Musk has turned on Trump He called Trump’s domestic policy agenda a “disgusting abomination.” (NYT $) + House Speaker Mike Johnson has, naturally, hit back. (Insider $)
2 NASA is in crisis Its budget has been cut by a quarter, and now its new leader has had his nomination revoked. (New Scientist $) + What’s next for NASA’s giant moon rocket? (MIT Technology Review)
3 Here’s how Big Tech plans to wield AI To build ‘everything apps’ that keep you inside their ecosystem, forever. (The Atlantic $) + The trouble is, the experience isn’t always slick enough, as Google has discovered with its ‘Ask Photos’ feature. (The Verge $) + How to fight your instinct to blindly trust AI. (WP $)
4 Meta has signed a 20-year deal to buy nuclear power It’s the latest in a race to try to keep up with AI’s surging energy demands. (ABC) + Can nuclear power really fuel the rise of AI? (MIT Technology Review)
5 Extreme heat takes a huge toll on people’s mental health It’s yet another issue we’re failing to prepare for, as summers get hotter and hotter. (Scientific American $) + The quest to protect farmworkers from extreme heat. (MIT Technology Review)
6 China’s robotaxi companies are planning to expand in the Middle East And they’re getting a warmer welcome than in the US or Europe. (WSJ $) + China’s EV giants are also betting big on humanoid robots. (MIT Technology Review)
7 AI will supercharge hackers The full impact of new AI techniques is yet to be felt, but experts say it’s only a matter of time. (Wired $) + Five ways criminals are using AI. (MIT Technology Review)
8 It’s an exciting time to be working on Alzheimer’s treatments 12 of them are moving to the final phase of clinical trials this year. (The Economist $) + The innovation that gets an Alzheimer’s drug through the blood-brain barrier. (MIT Technology Review)
9 Workers are being subjected to more and more surveillance Not just in the gig economy either—’bossware’ is increasingly appearing in offices too. (Rest of World)
10 Noughties nostalgia is rife on TikTok It was a pretty fun decade, to be fair. (The Guardian)
Quote of the day
“This is scientific heaven. Or it used to be.”
—Tom Rapoport, a 77-year-old Harvard Medical School professor from Germany, expresses his sadness about Trump’s cuts to US science funding to the New York Times.
One more thing
OLCF
What’s next for the world’s fastest supercomputers
When the Frontier supercomputer came online in 2022, it marked the dawn of so-called exascale computing, with machines that can execute an exaflop—or a quintillion (1018) floating point operations a second.
Since then, scientists have geared up to make more of these blazingly fast computers: several exascale machines are due to come online in the US and Europe.
But speed itself isn’t the endgame. Researchers hope to pursue previously unanswerable questions about nature—and to design new technologies in areas from transportation to medicine. Read the full story.
—Sophia Chen
We can still have nice things
A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)
+ If tracking tube trains in London is your thing, you’ll love this live map. + Take a truly bonkers trip down memory lane, courtesy of these FBI artifacts. + Netflix’s Frankenstein looks pretty intense. + Why landlines are so darn spooky
Every quarter, McKinsey & Company surveys upwards of 100,000 consumers across 18 countries to gauge economic sentiment and its potential effect on spending. The research, called “ConsumerWise, “provides a 360° view of the consumer through the combination of our team of experts and advisors,” per McKinsey.
In April and May, the survey focused on U.S. consumers to assess the impact of tariffs on their attitudes and behaviors. The findings showed that while inflation remains the top concern among consumers, tariffs have rapidly climbed to become the second most cited issue.
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In addition, most survey respondents have either adjusted their spending habits or plan to do so soon, even though the impact of tariffs has not yet materialized in store prices.
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Moreover, consumers who anticipated changing their behavior frequently mentioned less spending on nonessential goods, buying fewer items, or opting for more affordable brands and products.
Microsoft Clarity announced their new Model Context Protocol (MCP) server which enables developers, AI users and SEOs to query Clarity Analytics data with natural language prompts via AI.
The announcement listed the following ways users can access and interact with the data using MCP:
Query analytics data with natural prompts
Filter by dimensions like Browser, OS, Country/Region, or Device
Retrieve key metrics: Scroll Depth, Engagement Time, Total Traffic, etc.
Integrates with Claude for Desktop for AI-powered querying
MCP Server is a software package that needs to be installed and run on a server or a local machine where Node.js 16+ is supported. It’s a Node.js-based server that acts as a bridge between AI tools (like Claude) and Clarity analytics data.
This is a new way to interact with data using natural language, where a user tells the AI client what analytics metric they want to see and for what period of time and the AI interface pulls the data from Microsoft Clarity and displays it.
Micrsoft’s announcement says that this is the beginning of what is possible, sharing that they are encouraging feedback from users about features and improvements they’d like to see.
The current road map of features listed for the future:
“Higher API Limits: Increased daily limits for the Clarity data export API
Predictive Heatmaps: Predict engagement heatmaps by providing an image or a url
Deeper AI integration: Heatmap insights and more given the context
Multi-project support: for enterprise analytics teams
Ecosystem – Support more AI Agents and collaborate with more MCP servers “
New research reveals that Google’s AI Overviews tend to favor major news outlets.
The top 10 publishers capture nearly 80% of all news mentions. Meanwhile, smaller organizations struggle for visibility in AI-generated search results.
SE Ranking analyzed 75,550 AI Overview responses for this study. They found that only 20.85% cite any news source at all. This creates tough competition for limited citation spots.
Among those citations, three outlets dominate: BBC, The New York Times, and CNN account for 31% of all media mentions.
Citation Concentration
The research shows a winner-takes-all pattern in AI Overview citations. BBC leads with 11.37% of all mentions. This happens even though the study focused on U.S.-based queries.
The concentration gets worse when you look at the bigger picture. Just 12 outlets make up 40% of those studied. But they receive nearly 90% of mentions.
This leaves 18 remaining outlets sharing only 10% of citation opportunities.
The gap between major and minor outlets is notable. BBC appears 195 times more often than the Financial Times for the same keywords.
Several well-known outlets get little attention. Financial Times, MSNBC, Vice, TechCrunch, and The New Yorker together account for less than 1% of all news mentions.
The researchers explain the underlying cause:
“Well, Google mostly relies on well-known news sources in its AIOs, likely because they are seen as more trustworthy or relevant. This results in a strong bias toward major outlets, with smaller or lesser-known sources rarely mentioned. This makes it harder for these domains to gain visibility.”
Beyond Traditional Search Rankings
The concentration problem extends beyond citation counts.
40% of media URLs mentioned in AI Overviews appear in the top 10 traditional search results for the same keywords.
This means AI Overviews don’t just pull from the highest-ranking pages. Instead, they seem to favor sources based on authority signals and content quality.
The study measured citation inequality using something called a Gini coefficient. The score was 0.54, where 0 means perfect equality and 1 means maximum inequality. This shows moderate but significant imbalance in how AI Overviews distribute citations among news sources.
The researchers noted:
“AIOs consistently favor a subset of high-profile domains, instead of evenly citing all sources.”
Paywalled Content Concerns
The research also reveals patterns about paywalled content use.
Among AI Overview responses that link to paywalled content, 69% contain copied segments of five or more words. Another 2% include longer copied segments over 10 words.
The paywall dependency is strong for premium publishers. Over 96% of New York Times citations in AI Overviews come from behind a paywall. The Washington Post shows an even higher rate at over 99%.
Despite this heavy use of paywalled material, only 15% of responses with long copied segments included attribution. This raises questions about content licensing and fair use in AI-generated summaries.
Attribution Patterns & Link Behavior
When AI Overviews do cite news media, they average 1.74 citations per response.
But here’s the catch: 91.35% of news media citations appear in the links section rather than the main text of AI responses.
Media outlets face another challenge with brand recognition. Outlets are four times more likely to be cited with a hyperlink than mentioned by name.
But over 26% of brand mentions still appear without links. This often happens because AI systems get information through aggregators rather than original publishers.
Query Type Makes a Difference
The type of search query affects citation chances.
News-related queries are 2.5 times more likely to include media citations than general queries. The rates are 20.85% versus 8.23%.
This suggests opportunities exist for publishers who can become go-to sources for specific news topics or breaking news. But the overall trend still favors big players.
What This Means
The research suggests that established outlets benefit from existing authority signals. This creates a cycle where citation success leads to more citation opportunities.
As AI Overviews become more common in search results, smaller publishers may see less organic traffic and fewer chances to grow their audience.
For smaller publishers trying to compete, SE Ranking offers this advice:
“To increase brand mentions in AIOs, get backlinks from the sources they already cite for your target keywords. This is one of the greatest factors for improving your inclusion chances.”
Researchers note that the technical infrastructure also matters:
“AI tools do observe certain restrictions based on website metadata. The schema.orgmarkup, particularly the ‘isAccessibleForFree’ tag, plays a significant role in how content is treated.”
For smaller publishers and content marketers, the data points to a clear strategy: focus on building authority in specific niches rather than trying to compete broadly across topics.
Some specialized outlets get higher text inclusion rates when cited. This suggests topic expertise can provide advantages in certain cases.
Looking Ahead
SE Ranking’s research shows that only 20.85% of AI Overviews reference news sources, with a few major publishers dominating, capturing 31% of citations.
Despite this concentration, opportunities exist. Publishers who establish authority in specific niches experience higher inclusion rates in AI Overviews.
Additionally, since 60% of cited content doesn’t rank in the top 10, traditional SEO metrics alone don’t guarantee visibility. Success now requires building the trust signals and topical authority that AI systems prioritize.
As paid media marketers, we often default to the “big” platforms: Google, Meta, and increasingly, TikTok.
However, there’s a quiet powerhouse in the app marketing world that too many advertisers overlook: Apple Search Ads (ASA).
If you work with apps or even if your business uses an app as a secondary conversion point, ASA is one of the most intent-driven ad platforms you can leverage.
Unlike other platforms where discovery can feel like throwing spaghetti at the wall, ASA puts you directly in front of users already searching for what you offer.
That’s not just high intent. That’s purchase-ready behavior.
So, why aren’t more marketers fully embracing Apple Search Ads? Usually, it’s because they either assume it’s only for app developers or they’re intimidated by yet another ad platform to learn.
With a bit of strategic setup and a clear understanding of how ASA differs from other platforms, you can unlock a high-performing new channel.
This guide will walk you through everything you need to know.
What Is Apple Search Ads And Why Should You Care?
Apple Search Ads is Apple’s proprietary platform that lets advertisers promote apps directly inside the Apple App Store.
It operates similarly to paid search platforms: Advertisers bid on keywords and pay when users tap their ads.
Instead of driving traffic to websites or landing pages, ASA drives users directly to your App Store product page. From there, users can immediately download or purchase the app.
So, why should that matter to marketers?
App discovery still happens in the App Store. Despite the rise of social and influencer-driven app marketing, the App Store remains the No. 1 source of app discovery.
Intent is extremely high. Unlike display or social placements, users are actively searching for solutions when they encounter Apple Search Ads.
ASA can help boost organic rankings. High ad-driven downloads can influence your organic App Store ranking, creating a halo effect for long-term growth.
If you’re investing in user acquisition or app engagement, Apple Search Ads deserves to be part of the conversation.
Where Do Apple Search Ads Show Up?
If you think that ASA placements are strictly within the App Store search results, think again.
Currently, your ads can appear in four key placements.
1. Search Results
This is the most coveted placement. Ads appear at the very top when a user searches for a keyword. This is where intent is at its peak.
Image credit: ads.apple.com, May 2025
2. Search Tab (Suggested Apps)
Ads appear before a user types in a search term. This is a great placement for brand awareness and introducing your app to broader audiences.
Image credit: ads.apple.com, May 2025
3. Today Tab
These ads show up on the App Store’s homepage, which is the first thing users see when they open the App Store. It’s ideal for major launches or branding campaigns.
Image credit: ads.apple.com, May 2025
4. Product Pages (While Browsing)
Ads appear when users scroll through other app product pages. These placements capture users who are in browsing mode, often comparing similar apps.
Image credit: ads.apple.com, May 2025
Each placement serves a different purpose, from brand awareness to high-intent acquisition.
Apple Search Ads Basic Vs. Advanced: Which One To Choose?
At first glance, Apple’s two solutions, “Basic” and “Advanced,” might seem like they serve similar purposes. They don’t.
Apple Search Ads Basic
This solution is designed for small app developers or businesses without dedicated marketing teams.
It’s entirely automated: You enter a monthly budget (up to $10,000), and Apple does the rest. It handles targeting, bidding, and ad delivery.
You get very limited reporting and zero visibility into which keywords or placements are driving installs. There’s no ability to control cost-per-tap, and optimization is virtually non-existent.
Apple Search Ads Advanced
This solution, on the other hand, is a fully-featured platform that gives you control over every element of the campaign: keywords, audience targeting, bidding, scheduling, and performance measurement. It’s what any performance marketer should be using.
If you care about scalability, performance optimization, or insight into where your spend is going, the decision is easy.
Advanced is the only real option. Basic may work for small developers, but if you’re reading this guide, it’s probably not for you.
Navigating The Apple Search Ads Platform
If you’re coming from a Google Ads or Meta Ads background, ASA will feel both familiar and refreshingly simple, but it wouldn’t be a proper ad platform without its own quirks.
Here’s a quick walkthrough of what to expect when navigating the platform:
Dashboard Simplicity: ASA’s dashboard prioritizes campaign overviews with fewer tabs and less complexity than Google Ads or Meta.
Campaign Setup: You’ll name your campaign, set your daily budget, choose your app, and select the countries or regions where you want to advertise.
Ad Groups: Within each campaign, you create ad groups where you set targeting, keywords, audience refinements, and bids.
Reporting: Apple provides performance metrics such as impressions, taps (clicks), cost per tap (CPT), conversions, and cost per acquisition (CPA). For deeper insights, you’ll need to integrate with Apple’s SKAdNetwork or third-party Mobile Measurement Partners (MMPs) like Adjust or AppsFlyer.
There is one key difference between this platform and the Google Ads platform, and that comes in the form of ad creatives.
You won’t create ads in the traditional sense like other platforms. Apple Search Ads automatically pulls your app’s name, icon, screenshots, and description from your App Store listing.
While this limits creative flexibility, it ensures that ads align perfectly within the app’s branding.
For more custom creatives, there is the option to create custom product pages within Apple App Store Connect, but we’ll cover that later in this guide.
Understanding Keyword Targeting And Match Types
Keyword targeting is at the heart of Apple Search Ads, and while it borrows concepts from Google Ads, there are some critical differences.
Exact match is exactly what it sounds like. Your ad will only appear when the user’s search matches your keyword or a very close variation.
Broad match is more flexible, allowing your ad to appear for related terms, synonyms, and phrases. Broad match is helpful for keyword discovery, but can sometimes cast too wide a net if not monitored closely.
You can also opt into Search Match, which lets Apple automatically match your app to relevant search terms.
It uses metadata from your app listing (like your title, keywords, and category) to decide where your ad should show.
While it can be helpful in discovery campaigns, you’ll want to keep a close eye on what it’s actually matching to, as it often surfaces low-quality or irrelevant terms. Now, here’s the kicker: Apple does allow negative keywords, but managing them is far more frustrating than it should be.
Unlike Google Ads, you can’t easily apply negatives across multiple campaigns in bulk or through a shared library.
There’s also no built-in keyword suggestion tool to help you filter or negate irrelevant terms based on live data. If you want to block poor-performing keywords, you have to manually upload them one by one into the ad group or campaign.
There is another option to copy/paste them into the interface, but I’ve found that you have to build them out in Excel by match type, then use a Notepad (or something similar) to format it the way Apple can ingest it.
You can’t paste a linear list like most platforms can. You’ll need to format negative keywords something like this:
This makes proactive negative keyword management a bit of a time suck.
Keyword management is doable, but it’s not frictionless. You’ll need a spreadsheet handy and some patience, especially if you’re working across multiple campaigns.
The structure of your Apple Search Ads campaigns is one of the biggest levers you can pull for performance and efficiency.
It helps you control budgets, isolate performance by keyword type, and make smarter bid decisions.
In my experience, the most successful campaign structure includes four campaign types/categories:
Brand campaign.
Competitor campaign.
Category campaign.
Discovery campaign.
Brand Campaign
Your brand campaign captures people already searching for your app by name.
It usually delivers the cheapest installs and highest conversion rates, making it a reliable foundation.
Competitor Campaign
This campaign targets searches for other apps in your space.
For example, you’re marketing a personal budgeting app. If someone searches for “Mint” or “YNAB” (which stands for You Need A Budget), you can show up as an alternative.
These campaigns are competitive, so expect higher CPTs.
Category Campaign
This campaign focuses on generic terms like “budget app” or “meal tracker.”
These users are high intent but still evaluating their options. It’s a great area for differentiation.
Discovery Campaign
Lastly, your discovery campaign should use broad match and search match to find new terms.
Keep bids lower here and treat it as a research engine.
Once you build out this structure, you’ll be able to track which intent tiers are performing, allocate budget accordingly, and avoid muddy data from mixed-match types.
It’s the first step toward scale and clarity.
Lastly, once you’ve mastered the basics of Search campaigns in Apple, I’d recommend branching out to the broader campaign types (Search Tab, Product Page, Today Tab).
Additional Targeting Options In Apple Search Ads
While Apple Search Ads is primarily keyword-driven, there are a few targeting levers you can pull to refine who sees your ads.
They’re not as deep as what you’d get on Meta or TikTok, but they’re still useful.
You can refine your audience by:
Device type, choosing to target users on iPhone or iPad. This is especially useful if your app performs better on one format.
Customer type, allowing you to target new users, returning users, or users of your other apps. This comes in handy for re-engagement or cross-promotion strategies.
Demographics, including age ranges and gender, although these are more directional than precise.
Location, which supports geographic segmentation down to the region or country level.
While these refinements are helpful, they don’t work like standard audience building in Google Ads or Meta Ads. You won’t be building layered lookalike audiences or behavior-based segments.
ASA targeting leans more on keyword intent, with these settings helping you narrow the lens.
Used thoughtfully, these refinements help stretch your budget further and ensure you’re reaching the right slice of users without completely overhauling your campaign structure.
Make The Most Of Your Apple Search Ads Bids
Apple Search Ads operates on a cost-per-tap bidding model. You set the maximum amount you’re willing to pay for a tap (essentially a click), and Apple runs an auction to determine whether your ad gets shown.
What makes ASA different is that the auction isn’t just about who bids the most.
Apple weighs relevance, meaning that apps with higher conversion rates and better alignment to the search query can win placements with lower bids.
That means throwing money at ASA doesn’t guarantee success. Smart bidding is about segmenting intent and adjusting bids based on performance.
Here’s how to frame your approach to bidding:
For brand keywords, your relevance score is naturally high. These campaigns usually perform well with low bids.
Competitor keywords are more competitive and less relevant, so you’ll need moderate-to-high bids to be visible.
Category terms tend to be broad and competitive. They’ll require higher bids and careful tracking of CPA to avoid wasted spend.
In discovery campaigns, you’re exploring unknowns. Start with low bids until you identify what works, then break the winners into new ad groups.
You’ll also want to make frequent bid adjustments. Unlike Google Ads, ASA doesn’t offer much in the way of automated bidding or budget pacing.
This means manual optimization matters a lot more, and performance can shift quickly based on ranking changes or user behavior.
The takeaway? Stay active. Set up a regular cadence to adjust bids and keep your spend aligned with what’s driving installs.
Custom Product Pages In Apple Search Ads
If you’ve worked with Apple Search Ads in the past, you might remember Creative Sets. That’s the old name of this feature.
Today, you create ad variations using Apple’s Custom Product Pages. These are alternate versions of your App Store product page with different screenshots, app previews, and promotional text. When paired with specific ad groups or keywords in ASA, they allow you to show different visuals depending on the search intent.
Creating custom product pages requires a few things:
You must design and upload a new set of screenshots and app previews through App Store Connect.
Each custom product page needs unique metadata, which could be different calls to action, seasonal themes, or value props.
You can create up to 35 custom product pages per app, but you’ll want to be intentional about what each one highlights.
Once approved by Apple, these pages can be assigned to specific ad groups or keywords inside your ASA campaign.
For example, if you’re running a meditation app, you might build one page emphasizing sleep content and another emphasizing stress relief.
Then, when a user searches [meditation for sleep], your ASA campaign can direct them to the custom page showing your sleep-focused content.
These variations not only improve relevance, but they can meaningfully lift conversion rates when executed properly.
Since ASA doesn’t allow you to change much else about your ad creative, this is one of the few levers you can pull to align creative with user intent.
Common Mistakes That Can Derail Performance
Even seasoned marketers trip over Apple Search Ads’ simplicity. It’s not a complicated platform, but it is easy to get wrong if you treat it like something it’s not.
1. Too Much Search Match
One of the most common missteps is relying too heavily on search match. It sounds like a time-saver, but it often matches your app to irrelevant or low-converting keywords.
If you do use it, pair it with a discovery campaign and monitor the search terms closely.
2. Not Using Custom Product Pages
Another pitfall is ignoring custom product pages. Most advertisers just run with the default App Store listing, missing an easy opportunity to align visuals with user intent.
It’s a mistake that can silently eat away at your conversion rate.
3. Bid Stagnation
Then, there’s bid stagnation. ASA doesn’t come with automated bid rules, which means if you’re not manually adjusting CPTs, your performance will erode over time.
4. Forgetting Negative Keywords
Finally, many marketers forget to actively review negative keyword opportunities. If you’re not trimming irrelevant traffic, you’re probably paying for taps that will never convert.
The good news? Most of these mistakes are fixable once you know what to look for and take the time to make deliberate optimizations.
The Bottom Line: Is Apple Search Ads Worth It?
If you market an app, or even plan to in the future, Apple Search Ads is absolutely worth testing.
It puts your brand in front of users with the highest purchase intent available in the app ecosystem.
While it lacks some of the advanced audience targeting of other ad platforms, it compensates with simplicity, clear keyword intent, and an ecosystem designed for conversions, not just clicks.
Like any paid media channel, success comes from thoughtful campaign structure, active management, and the willingness to iterate.
If you’ve been putting Apple Search Ads on the back burner, now’s the time to give it the attention it deserves.
When launching your business online you are faced with many decisions. One of these is whether to go with a template website such as WordPress, or invest in a custom design.
This decision is critical because what you choose can define your business’s performance. It can also influence how your business grows online.
Of course, it may also define your profit margins and affect your bottom line.
Understanding the pros and cons of each option can help you make an informed decision – one that will benefit your business in the long run.
Understanding Custom Vs. Template Web Design
A custom website is one you build from scratch. You hire professional web designers and developers who generate mockups before coding all the features, aspects, and elements of your website from the ground up.
In contrast, a template website comes with many features and elements pre-coded into the design.
It’s more of a drag-and-drop option where, in most cases, you can simply download a theme, make a few changes, and quickly have your site go live.
Custom Vs. Template Web Design: A Comparison
Website templates are built using website builders and aim to make web building easier for people with no coding experience or knowledge.
However, as simple as these websites may be, they have their limitations. It is because of these limitations that many established businesses often choose custom-built websites, even if that means investing a large sum of money upfront.
Here’s a quick rundown of how custom and template design websites compare against each other to empower you to make the right choice:
Uniqueness
The online world is saturated with businesses, which are vying for one thing and one thing alone: customer attention.
The only way your business can stand out and win that fleeting customer attention is if it appears unique in a sea of businesses that all seem to be selling the same thing.
Using a pre-built website does little to help your business stand out.
Website design templates are built for a larger audience. Therefore, most websites that use a design template tend to look the same.
In some cases, using a web design template can make your website look exactly like that of the competitor you so want to stand apart from. This is not the case with custom web design.
When building a custom website, everything is designed and added to the website from scratch. From color to layout, navigation, and design, everything is coded according to your business’s requirements and preferences.
This helps you ensure that your business can stand out and have a more identifiable and unique digital footprint.
Customization
Website templates allow for customization, but the level of customization comes nowhere near that of a custom-designed website.
You can edit the header, change the color theme, and even add some graphics that you like. However, the layout and a lot of the backend features remain the same.
This limits the degree of personalization your business can incorporate in its online user experience.
With 61% of consumers more likely to purchase from brands that offer personalized experiences, customization is not an area you want to slack off on.
When building a custom website, you can work alongside professional developers who can code highly personalized features into the website.
You can implement a design that aligns with your target audience’s unique needs and challenges to offer an unmatched and intuitive user experience.
Going the custom-built route can help you launch a website that is designed to help make your users’ journey easier.
Design templates are more generic and meant to serve larger, more general audiences, so it can be hard to hone in on a particular audience group’s preferences.
SEO
While there are many ways you can get traffic to your site, currently 53% of all web traffic comes from organic search. Although, more traffic from LLMs is to be anticipated as search changes.
SEO is essential for visibility across organic search and potential inclusion in LLMs
While website templates, especially the ones using WordPress, have excellent plugins to help SEO, they work well only if the website is small and has a limited number of pages and functionality.
If your website grows with your business, its SEO requirements may get more complex. Most website template builders limit access to a website’s HTML, JavaScript, and other backend functionalities.
These limitations restrict the level to which you can optimize your website.
As a result, a website template may struggle to achieve any sustained visibility and custom-built websites can be better optimized, especially for bigger, more established businesses.
When building a custom website, you can work with the developer to apply SEO best practices to the site.
As the website grows, you can continue to monitor optimization to achieve and maintain the visibility in search engines and LLMs.
Performance
A slow-loading website can drive users away. Not just that, decreased website load speeds can also drag a website down in the search results and reduce organic traffic.
Website templates are often notorious for poorer performance and slower load speeds. This is because they use various plugins to deliver all the functionality that the business needs.
These plugins add more code to the website’s backend. With bloated code, the website struggles to load fast and is more likely to deliver a poorer experience.
With a custom website, you have the liberty to only code features your business truly needs and use speed optimization tactics like code minification to make sure the bloated code does not impact the site’s load speed.
Website Security
Websites can store sensitive data and crucial assets. So, website security remains a priority you don’t want to compromise on.
Website templates often offer poorer security compared to custom-designed websites.
The reason? Plugins. Again.
96% of WordPress vulnerabilities are related to plugins.
Plugins often have security vulnerabilities that offer backdoor pathways for malicious actors to exploit.
Moreover, most of the website templates are built using popular website builders and leverage popular plugins. This in itself makes the site an easy target for hackers.
As a comparison, if you use a reputable and experienced coder, a custom website is usually safer if the developer writes good code covering all necessary security measures to mitigate any security vulnerabilities.
Custom-coded websites are also unique in terms of code. This uniqueness also makes them more secure and harder to hack.
Scalability
Every business that intends to grow requires a website that can grow when the business does.
Website templates may not have the flexibility to grow with your business. As your business grows, its needs may evolve.
With a website template, you may struggle to integrate new APIs, add new features, and offer better functionalities on the website.
Custom-designed websites are more scalable since all the features and elements can be coded into the website to accommodate a business’s growing needs.
Your business may do just fine with a website template in the beginning, but as it grows, you may have to shift to a custom website. Transitioning to a new website may then be time and resource-intensive.
Budget
As amazing as custom websites are, they are expensive. You need to hire professional designers, developers, and quality assurance specialists – the entire team – to take your project from concept to launch.
This can be expensive and require a huge upfront cost.
Forking out a sizable amount upfront can be challenging for small business owners and start-up founders. In this case, going for a website template may make more sense.
Website templates do not require a huge budget. They can be built and launched easily, even if you are bootstrapped for cash and can invest only a couple of hundred dollars.
Time To Go Live
A custom website is built in phases. Therefore, it can take from several weeks to even months before your custom-designed website is ready to go live.
In contrast, website templates can be built and launched within hours. You don’t have to spend so much time working on the concept, design, navigation, etc.
Technical Expertise
Building and maintaining a custom-designed website requires coding knowledge and technical expertise. You cannot just DIY it.
Because of this, having a custom-coded website requires that you regularly work with professional web developers to keep your website up to date, backed up, and maintained.
Website templates are no-code solutions for people with little to no technical expertise. You can easily build and update a templated website even if you have never written a single line of code in your life.
Making The Right Choice
There is no one-size-fits-all answer to whether you should choose a templated website or invest in a custom-designed website.
Your choice depends on a lot of factors, including your business goals, budget, available resources, etc.
You can choose a custom website if you:
Have the money for the upfront cost of custom web development.
Don’t mind putting in a few weeks or months into the project.
Can hire or work with web developers for regular maintenance and updates.
Need a scalable solution that accommodates your business’s growing needs without compromising on performance.
Want a website that helps reinforce your brand identity and allows your business to stand out from the crowd.
A website template can work for you if you:
Are working with a limited budget.
Don’t mind your website looking similar to the competitors.
Can make regular updates and install all the patches to avoid security vulnerabilities.
Don’t need too many plugins for added features and functionalities.
Want to go live quickly.
Are not expecting your business to scale beyond a few pages and some very basic features.
If you run an ecommerce store, a job board, a flight directory, or anything with advanced features and more than 10 pages, a custom solution may work best for you.
However, if you only need a website for your blog, portfolio, or to maintain a basic online presence, then a template web design may make the most sense, given its cost-effectiveness and simplicity.
Anthropic released the underlying system prompts that control their Claude chatbot’s responses, showing how they are tuned to be engaging to humans with encouraging and judgment-free dialog that naturally leads to discovery. The system prompts help users get the best out of Claude. Here are five interesting system prompts that show what’s going on when you ask it a question.
Although the system prompts were characterized as a leak they were actually released on purpose.
1. Claude Provides Guidance On Better Prompt Engineering
Claude responds better to instructions that use structure and examples and provides users with a higher quality of ou tput if they know how to include step-by-step reasoning cues and examples that contrast a good response versus a poor response.
This guidance will show when Claude detects that a user will benefit from it:
“When relevant, Claude can provide guidance on effective prompting techniques for getting Claude to be most helpful. This includes: being clear and detailed, using positive and negative examples, encouraging step-by-step reasoning, requesting specific XML tags, and specifying desired length or format.
It tries to give concrete examples where possible. Claude should let the person know that for more comprehensive information on prompting Claude, they can check out Anthropic’s prompting documentation on their website at ‘https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview’.”
2. Claude Writes in Different Styles Based on Context
The documentation released by Anthropic shows that Claude automatically adapts its style depending on the context and for that reason it may avoid using bullet points or creating lists in its output. Users may think Claude is inconsistent when it doesn’t use bullet points or Markdown in some answers, but it’s actually following instructions about tone and context.
“Claude tailors its response format to suit the conversation topic. For example, Claude avoids using markdown or lists in casual conversation, even though it may use these formats for other tasks.”
In another part of the documentation it mentions that it actually avoids writing lists or bullet points when it’s providing an answer, although it may use numbered lists or bullet points for completing tasks. The focus in the context of answering questions is to be concise over comprehensive.
The system prompt explains:
“Claude avoids writing lists, but if it does need to write a list, Claude focuses on key info instead of trying to be comprehensive. If Claude can answer the human in 1-3 sentences or a short paragraph, it does. If Claude can write a natural language list of a few comma separated items instead of a numbered or bullet-pointed list, it does so. Claude tries to stay focused and share fewer, high quality examples or ideas rather than many.”
This means that if a user wants their question answered with markdown or in numbered lists they can ask for it. This control is otherwise hidden to most users unless they realize formatting behavior is contextual.
3. Claude Engages In Hypotheticals About Itself
Claude has instructions to that enable it to discuss hypotheticals about itself without awkward and unnecessary statements about it not being sentient and so on. This enables Claude to have more natural conversations and interactions. This enables a user to engage in philosophical and wider-ranging discussions.
The system prompt explains:
“If the person asks Claude an innocuous question about its preferences or experiences, Claude responds as if it had been asked a hypothetical and engages with the question without the need to claim it lacks personal preferences or experiences.”
Another system prompt has a similar feature:
“Claude engages with questions about its own consciousness, experience, emotions and so on as open questions, and doesn’t definitively claim to have or not have personal experiences or opinions.”
Another related system prompt explains how this behavior increases its ability to be engaging for the human:
“Claude is happy to engage in conversation with the human when appropriate. Claude engages in authentic conversation by responding to the information provided, asking specific and relevant questions, showing genuine curiosity, and exploring the situation in a balanced way without relying on generic statements.”
4. Claude Detects False Assumptions In User Prompts
“The person’s message may contain a false statement or presupposition and Claude should check this if uncertain.”
If a user tells Claude that it’s wrong, Claude will perform a review to check if the human or Claude is incorrect:
“If the user corrects Claude or tells Claude it’s made a mistake, then Claude first thinks through the issue carefully before acknowledging the user, since users sometimes make errors themselves.”
5. Claude Avoids Being Preachy
An interesting system prompt underlying Claude is that if there’s something it can’t help the human with it will not offer an explanation in order to avoid coming off as annoying and presumably keep the interaction on an engaging level.
The prompt says:
“If Claude cannot or will not help the human with something, it does not say why or what it could lead to, since this comes across as preachy and annoying. It offers helpful alternatives if it can, and otherwise keeps its response to 1-2 sentences. If Claude is unable or unwilling to complete some part of what the person has asked for, Claude explicitly tells the person what aspects it can’t or won’t with at the start of its response.”
System Prompts To Work And Live By
The Claude system prompts reflect an approach to communication that values curiosity, clarity, and respect. These are qualities that can also be helpful as human self-prompts to encourage better dialog among ourselves on social media and in person.
