How should developer-tool companies approach AEO?

What do developers ask AI assistants about tools?

Mostly task questions, tool-choice questions and migration questions, and each kind needs a different page.

A lot of developer discovery starts with a task. Someone asks how to add rate limiting to an API, and the answer includes a code sample that imports a particular library. That library has just been recommended without anyone asking for a recommendation. A docs page that answers a task well can earn mentions there as well as in tool-choice answers.

How do you turn docs into answer pages?

Give each common task its own page, open with one sentence that answers it, and follow with a code sample that runs.

Most docs are organized by feature: 'Middleware', 'Configuration', 'Adapters'. Developers ask by task: 'how do I verify a webhook signature in Next.js'. Add task pages that use the developer's wording as the title, and link them to the reference pages underneath.

Keep a public, dated changelog. When a limitation is fixed, the changelog entry and the updated docs page are the public record that it changed. Without them, the newest public word may be an old thread saying the feature is missing.

• Title the page with the task as a developer would type it, including the framework or language.
• Answer in the first one or two sentences: which package, which function, and any version requirement.
• State limits plainly: unsupported runtimes, rate limits, features on paid tiers only.
• Check that the page text is in the HTML the server returns, so a crawler can read it without running scripts.

Do comparison and alternatives pages help developer tools?

Yes, when they answer the comparison a developer actually types and are fair about where the other tool is the better pick.

A page titled '<your tool> vs <competitor>' with a table of concrete differences gives a reader, and an assistant summarizing the topic, something specific to repeat: license, self-hosting, supported languages, pricing model, and the date you last checked. Adjectives like 'faster' and 'simpler' give it nothing.

Developers notice one-sided comparisons quickly. Say where the competitor fits better, for example a larger plugin ecosystem or a hosted option you do not offer. That makes the page more credible, and more likely to be linked from threads about the choice.

What role do README files, GitHub and Stack Overflow play?

They are public text that developers read and AI engines can find, so they should answer the same questions your docs do.

Your README's first paragraph is often the first description anyone reads, so it should say what the project does, who it is for, the install command, and where the docs live. GitHub issues where someone asks 'does this support X?' deserve a direct answer and a link to the docs page, because those threads stay public and searchable for years.

On Stack Overflow, answer questions in your tag with code that works, and say that you work on the tool; Stack Overflow expects that disclosure. On Hacker News, a Show HN post and honest replies in Ask HN threads that ask for recommendations put your tool in public discussion. We cannot tell you how much weight any engine gives these sites. What you can check is whether their pages appear in the links your tracked answers cite.

MarketHQ monitors Hacker News (stories and comments), GitHub (issues and pull requests), Stack Overflow (new questions), Dev.to and Lobsters on every plan. Every plan checks them four times a day and picks up to three reply opportunities.

How do you check whether AEO work is paying off?

Re-ask the same developer questions every week and compare who gets named, which links get cited, and how your tool is described.

Keep 10 to 20 questions that mix the types above, worded the same every week. The description matters as much as the mention: an answer that names you but says 'no Python SDK' when you shipped one last quarter points straight at the docs and changelog.

MarketHQ's paid plans ask your tracked questions in ChatGPT, Claude, Gemini, Perplexity, Grok and DeepSeek every week. Each answer shows whether you were named, which competitors were named, the full text, and a Cites column with the links it used. Answers with no links are labelled 'Answered from memory, no sources', which tells you a new docs page may take longer to show up in that engine. For a quick first reading, the free AEO audit asks ChatGPT and DeepSeek two fixed prompts (the same for every domain) and checks for your name and domain.

What does AEO look like for one developer tool?

Illustrative example: a job-queue library finds that assistants answer a common task question with two competitors and an old forum thread.

Illustrative example: a background-job library for Node.js tracks 'how do I run scheduled jobs in a Node.js app'. Most engines name two competing libraries. The Cites column shows an old Stack Overflow thread and a competitor's docs page, and one answer says the library lacks cron syntax, which it added months ago.

The team writes a docs page titled 'Run scheduled jobs in Node.js', opening with the two-line answer and a working sample. It posts an updated answer on the Stack Overflow thread, disclosing that the author maintains the library, adds a dated changelog entry for cron support, and rewrites the README's first paragraph. It then watches the question for four weeks.

What can AEO not guarantee for a developer tool?

You cannot control what a model learned in training, and answers vary between runs and users.

Some answers come from the model's memory, where a fix on your site can take a long time to appear. MarketHQ asks through each engine's API, and a signed-in developer in the consumer app may see a different answer. It does not track Google AI Overviews, Google AI Mode or Microsoft Copilot, and it does not audit your docs or schema.

Nothing here is specific to developer tools in principle. Any company with documentation, a comparison page and active community threads can run the same method; the communities just change.