Do I need to rewrite my whole docs site to get recommended by Cursor or Copilot?

No. Start with the pages developers hit first: the README, the quickstart, and your top two or three integration guides. Restructure those for extractability, add a working code sample near the top, and ship an llms.txt file. That covers the majority of what retrieval-based assistants like Cursor actually pull from in a typical session.

Does GitHub Copilot recommend packages based on stars alone?

No. Stars are one weak signal among several, alongside commit recency, code quality in the training corpus, and how often developers accept suggestions involving that package. A well-maintained library with modest stars but excellent, current documentation and clean source code can still surface strongly in Copilot's suggestions.

Is llms.txt worth implementing if adoption is still under 20% of the web?

Yes, specifically for developer tools, because adoption is concentrated exactly where your buyers already are: documentation-heavy platforms, SDKs, and API providers. It costs little to implement and directly reduces the odds an AI agent hallucinates your API surface, which is a real risk to your brand independent of any citation benefit.

Does FAQ or HowTo schema still help after Google removed the rich results?

Yes, in a narrower way. The visual snippet in Google Search is gone, FAQ rich results were removed May 7, 2026, and HowTo results were removed back in 2023, but both remain valid schema.org types. AI crawlers and answer engines still use that markup to parse and extract structured Q&A and step-by-step content correctly, so it is worth keeping on docs and tutorial pages even without a SERP payoff.

Which matters more for a dev tool: brand-owned docs or third-party tutorials?

Both, and neither substitutes for the other. Community sources like Reddit and dev.to are cited more often than any single brand domain across major AI engines, but they usually reference and link back to your own docs for the authoritative detail. Weak brand docs undermine even strong third-party buzz, because the assistant has nowhere reliable to verify the claim.

How often should I re-run my dev-tool citation share audit?

Monthly is a reasonable cadence for most teams, since training snapshots update infrequently but retrieval-based results from MCP servers and live browsing can shift week to week as your docs and competitors' docs change. Increase frequency around major releases or when you specifically ship llms.txt, new docs structure, or a competitor launches a comparable tool.

Can I influence what ChatGPT already "knows" from training data?

Not directly and not immediately. Training data is a frozen snapshot from a point in the past, so today's changes will not retroactively alter it. What you can influence is the live layer: web browsing, MCP tool calls, and the Apps SDK tool descriptions, which is why the retrieval and repo-signal work in this guide matters more in the near term than waiting for the next training cutoff.

Do I need a paid monitoring tool to track this, or can I do it manually?

You can start manually with a spreadsheet of ten to twenty realistic prompts run by hand across two or three assistants once a month. That gets you a real baseline. It becomes worth automating once you are tracking citation share across multiple competitors, multiple assistants, and a growing prompt panel on a consistent schedule, which is where a dedicated visibility platform pays for itself.

How to Optimize for AI Coding Assistants and Dev-Tool Recommendations