Oct 12, 2023
5 min read

The Future of Technical Content in an AI World

As LLMs become the primary interface for developer knowledge, the role of static documentation is changing. Here is how to adapt your strategy.

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The Future of Technical Content in an AI World

The way developers consume information is shifting fundamentally. For the last two decades, the primary loop has been:

  1. Encounter a problem
  2. Google the error message or concept
  3. Scan Stack Overflow or documentation
  4. Copy, paste, adapt

With the rise of LLMs (Large Language Models), this loop is collapsing. Developers are increasingly asking their IDE or a chat interface to "fix this error" or "explain this concept."

What this means for documentation

Static documentation isn't dying, but its purpose is evolving. It is no longer just a reference for humans; it is the ground truth for AI.

If your documentation is unstructured, ambiguous, or out of date, LLMs will hallucinate or provide suboptimal answers about your product.

The new requirements

  • Semantic clarity: Content must be unambiguous to both humans and machines.
  • Contextual completeness: Code snippets need surrounding context to be effectively parsed.
  • API contracts: Your OpenAPI specs are now your most important marketing asset.

Adapting your strategy

To survive in this new world, developer-focused companies need to treat content as data. This means:

  1. Structured authoring: Moving away from free-form Markdown to more structured formats where applicable.
  2. Testing your content: Verifying that LLMs can accurately answer questions based on your docs.
  3. Feedback loops: Monitoring what developers are asking AI about your product to identify gaps.

The future belongs to those who build for the hybrid consumer: the human developer and the AI assistant.

"The documentation of the future isn't just read by humans. It's ingested by machines to answer human questions."

Specific actions to take today

Start by auditing your most popular documentation pages. Ask ChatGPT or Claude questions about those pages. If the answers are wrong, your content might be unstructured or ambiguous.

  • Add explicit prerequisites to every guide.
  • Ensure code samples are self-contained.
  • Use standard terminology consistently.