Documentation written for people now has machine readers too

A coding assistant produced an obsolete integration example because the current documentation lived behind client side navigation that its retrieval process did not parse well. The answer sounded plausible because the older version remained available in a repository mirror.

Stack Overflow’s 2025 survey found that 84 percent of respondents used or planned to use AI tools in development. The same survey found that 66 percent had encountered output that was almost right. Documentation quality now influences both human work and the material retrieved by assistants. In our judgment, the number describes demand while the surrounding evidence describes the constraint.

Stack Overflow also found that technical documentation was the most common learning resource. Human readers and machine readers share a need for stable definitions, clear examples, and visible version boundaries. This result changes the decision because it exposes the cost that a speed metric leaves out.

Structured sources help because they separate the content from a particular page layout. Formats such as JSON and llms-full.txt can be consumed by publishing tools or retrieval systems. Structure does not guarantee correctness, though it makes completeness and versioning easier to inspect.

In our judgment, documentation is part of the product. Its value appears when a reader can answer a consequential question without waiting for an expert to reconstruct context.

Machine readers change the unit of documentation. A person can infer that two paragraphs describe the same parameter even when their wording differs. Retrieval software benefits from explicit names, stable identifiers, and clear version boundaries. The requirement does not make prose less important. It makes structure more important alongside prose.

Doku follows this operating judgment. Doku accepts structured documentation sources and renders them as a navigable portal. The same source can support a readable site and a machine oriented representation, reducing the chance that people and software consult different versions.

This approach introduces editorial responsibility. Generated documentation can repeat one outdated comment across many pages. Teams still need owners and release checks. A common source makes the error easier to locate when behavior changes.

Our standard is empirical. Provenance becomes part of correctness. When an assistant quotes a deprecated option, the reader needs to know which version and source produced the answer. Structured files can carry that metadata. They can also distribute an error quickly when generated from stale source comments, which is why release checks remain necessary.

Freshness becomes harder when several representations exist. A human page, a JSON export, and a retrieval file can drift unless they are generated from the same release. When an assistant uses an older copy, the answer may sound precise while describing behavior that no longer exists. Versioned publication lets the reader trace the answer back to the product state it describes.

We hold a deliberately narrow view. Machine readable documentation is useful when it remains ordinary documentation first: specific, current, and tied to the product. Formatting cannot rescue uncertain facts.