Skip to main content

Developer Experience in the AI Era

5 min read
DevrelTech Writer

Devrel

Developers will use AI to learn your product. Design for that.

Tech Writer

AI will cite your docs. Make them accurate and structured.

Developer Experience in the AI Era

TL;DR

  • Developers use AI to explore, debug, and integrate. Your docs, APIs, and samples are training data — directly or indirectly.
  • DX in 2026: Optimize for "AI can find and use this" as well as "human can understand this."
  • Structure, accuracy, and crawlability matter more. Pretty prose matters less.

When a developer is stuck, they don't always read your docs. They ask ChatGPT. Or Cursor. Or Copilot. Those tools pull from your docs, your examples, your Stack Overflow threads. If your DX is a mess, AI will propagate that mess. If it's clear and structured, AI will help developers faster.

How Developers Use AI Today

Exploration:

  • "How do I get started with [your product]?" AI summarizes your getting-started guide. If it's wrong or outdated, the developer gets a bad first impression.
  • Your docs are the source of truth. Treat them that way.

Integration:

  • "Write code to call [your API]." AI generates from examples it's seen. Good examples = good generated code. Bad or missing examples = hallucinations.
  • Invest in copy-paste-ready samples. Keep them current.

Debugging:

  • "I'm getting error X from [your product]." AI suggests fixes. If your error messages are vague, AI will guess. Often wrong.
  • Clear errors, good docs, and a searchable knowledge base = better AI-assisted debugging.

What Good DX Looks Like Now

Structured and parseable:

  • Headers, sections, code blocks with clear labels. AI (and humans) can navigate.
  • Avoid walls of prose. Break it up. Use consistent patterns.

Accurate and current:

  • AI trains on what's out there. Stale docs poison the well. Version your docs. Deprecate clearly. Keep changelogs.
  • One source of truth. No contradictory pages.

Example-rich:

  • Every major use case should have a working example. Not "here's the concept" — "here's the code."
  • AI and humans both learn from examples. Provide them.

Error messages that help:

  • "Error 429" is useless. "Rate limit exceeded. Retry after 60 seconds. See [link]." is useful. AI can pass that through. Humans can act on it.
  • Design errors for both. Document them.

What You Need to Own

Discoverability:

  • Can AI (or a search engine) find your docs? Good URLs, sitemaps, structured data. SEO for docs.
  • "Documentation as a product" — treat it like one. Measure usage, search success, support ticket deflection.

The "first mile":

  • Getting started, auth, first API call. This is where developers form opinions. And where AI will try to help.
  • Make the first mile bulletproof. Test it monthly. Fix what breaks.

Feedback loop:

  • When AI gives a wrong answer that traces to your docs, that's a bug. Support and community are your QA.
  • Have a way to capture "docs are wrong" and act on it.

Manual process. Repetitive tasks. Limited scale.

Click "With AI" to see the difference →

Quick Check

What remains human when AI automates more of this role?

Do This Next

  1. Test your docs through AI — Ask ChatGPT or Claude: "How do I get started with [your product]?" See what it says. Fix the source if it's wrong.
  2. Audit your examples — Do they run? Are they versioned? Add one missing example for a common use case.
  3. Improve one error message — Pick your most common support-driver. Add context, a link, and a suggested fix. See if ticket volume drops.