Practical AI use, verification, automation, risks and the new documentation workflow.
AI is often more useful when it challenges structure, ambiguity and omissions than when it is treated as the authority that decides what is true. Separate language work from truth: a clearer sentence can still describe incorrect behavior.
Generative text can sound complete while quietly inventing defaults, guarantees or citations. Build a verification pass around authoritative sources, executable examples and explicit uncertainty. The dangerous AI error in documentation is rarely broken grammar.
In three years, generative AI turned a first draft from an expensive output into a cheap operation. The important question is no longer whether a model can write, but who owns facts, structure, review, updates, and quality once text can be produced in minutes.
If a model can create or update documentation at scale, validation must scale with it. Constrain inputs, preserve provenance, validate structure and make publication a separate privileged step. A useful architecture assumes the model will sometimes be wrong.
Generated examples can accelerate documentation work, but they must never be allowed to invent product behavior and then validate themselves. A code example is a factual claim about fields, values, order and behavior.