Introduction

The way people consume technical documentation is undergoing a fundamental shift. Instead of reading long manuals or web pages, users increasingly ask AI assistants (like ChatGPT or Claude) to find answers for them. In fact, by 2025 over half of people expect AI to replace traditional search engines, and developers now turn to tools like ChatGPT for help more often than they open official docs. Google’s integration of AI summaries into search results (reaching billions of users) underscores this new reality. This means the primary reader of documentation is becoming the AI system itself – which then interprets and relays information to the human user.

Why is this shift happening? AI assistants can deliver just-in-time, specific answers without requiring a human to manually search and skim multiple documents. They excel at synthesizing information from various sources and providing a concise response. However, an AI can only be as accurate and helpful as the documentation it has access to. If documentation isn’t written and structured in a way that AI systems can easily ingest and navigate, users will get outdated or incorrect answers (or none at all). Organizations are already observing cases where their guides don’t show up in chatbot answers or the bots quote obsolete info because the docs weren’t updated for AI consumption.

AI-first documentation is an inevitable evolution to address this gap. In an AI-first approach, we write documentation for the benefit of AI readers first, and human readers second. This does not mean humans never see the docs or that AI replaces documentation entirely. It means documentation is prepared in a format that optimizes AI understanding and retrieval – clear structure, unambiguous references, explicit metadata – so that any AI (from customer support bots to ChatGPT plugins) can find and present the right information on demand. High-quality, fact-based documentation remains as critical as ever; what’s changing is how that documentation is consumed and mediated. As one industry expert noted, LLMs are already part of users’ workflow, making AI-aware documentation the “new baseline” for delivering information.

This doctrine lays out a blueprint for this new era. It challenges traditional, human-first documentation practices and introduces principles to design content that is prompt-ready and AI-friendly from the start. It is intended for engineers, product leads, and architects who need to ensure their documentation will be correctly interpreted by AI systems (and thus correctly conveyed to users). The goal is to prevent the common pitfalls of AI-doc interactions – such as hallucinated answers, context confusion, or missed information – by fundamentally rethinking how we author technical content. In the sections that follow, we contrast the “old world” vs. “new world” of documentation, debunk myths, and establish core principles of AI-native technical writing. We then dive into practical guidelines: how to embed helpful cues and metadata in docs, how to break down information into retrievable modules, and how to implement pipelines that connect documentation to LLMs (Large Language Models) with accuracy and guardrails. Real-world case studies (using open-source projects and our own products) will illustrate what this doctrine looks like in practice. Finally, we provide checklists to help teams adopt these practices and for individual writers to create AI-ready content.

The core thesis is provocative but simple: for most complex products, the first “user” of your documentation is now an AI agent. Embracing that reality can dramatically improve the quality and timeliness of answers your actual customers and team members get. Ignoring it, on the other hand, risks making your documentation irrelevant. The following doctrine is our guide to navigating this transition and ensuring that documentation remains a first-class asset in the age of AI.