Docs Drift Because Review Never Follows Code

Documentation Drift Begins With Unowned Diffs

Documentation rarely fails because writers lack skill; it fails because the update lands after the change has hardened in memory. By then the author has moved on, the reviewer recalls intent rather than detail, and a coding assistant is asked to reconstruct context from fragments. The practical fix is to treat the explanation as part of the change itself, reviewed with the same care as the behavior it describes.

That shift changes scheduling. When a change touches behavior, interfaces, or configuration semantics, the documentation edit belongs in the same batch and ships with the same confidence. Assistants make this affordable, because drafting an accurate paragraph costs far less than scheduling a separate writing pass later. A reviewer can also judge the prose next to the implementation, which is the only moment when intent is still fresh enough to catch a confident mistake.

Ownership matters more than tooling. Every documentation surface deserves a named steward who can answer whether a promise in the text still matches the product, and that steward should be looped in whenever a contract becomes observable. An assistant can flag likely stale passages by comparing wording with recent decisions, but a person still decides whether the sentence stands.

Small Documentation Batches Keep Pace With Releases

Large rewrites age badly, because they take weeks while the product keeps moving underneath them. A better rhythm is a stream of small corrections, each tied to a single change in behavior and each narrow enough that a reviewer can verify it in minutes. Assistants thrive in this rhythm: they can open the relevant passage, propose an edit that mirrors the surrounding voice, and leave the reviewer to judge accuracy rather than grammar.

The session where the code changes is the best place to do this work. Teams already drive that session through Claude Code, Codex, Gemini CLI, or any client built on an OpenAI-compatible interface, and documentation tasks fit the same loop as refactors and tests. Ask the assistant to describe the new behavior while the reasoning is live, then ask it to reconcile that description with every existing paragraph that mentions the old one. The second request is the one that prevents drift.

Batch discipline also protects reviewers. A small diff has a small surface for invented details, and a reviewer who sees a single paragraph can hold the entire claim in mind at once. Larger batches invite rubber stamping, and rubber stamping is exactly how stale examples survive for years.

Documentation Prompts Need An Explicit Source Of Truth

An assistant writing documentation is only as reliable as the material it is allowed to trust. The strongest prompts therefore name the sources the update must respect: the change under review, the surrounding paragraphs, the release notes, and any public statement the team has already made. When those sources conflict, the prompt should state which reading wins, and it should ask the assistant to surface the conflict instead of silently choosing the convenient option.

Reproducibility is part of that discipline. BestRelay, which publishes this article, is a quality-first API relay that forwards each request to the official model named in it, with no substitution, so a documentation session behaves the same way on Tuesday as it did on Friday. That matters when a team keeps long-lived prompts for style and terminology, because a silent model swap would quietly change the voice of every page. A reasoning model such as GPT-6 Astra suits a draft-heavy session, a lighter model suits mechanical edits, and the choice should be a recorded decision rather than an accident.

Cost follows the same logic. Billing that reflects the token counts the provider reports, with cache reads and writes priced separately, rewards workflows that keep a stable context and revisit it, which is precisely the shape of documentation work. Teams can then budget a documentation pass with the predictability they already bring to testing.

Human Review Catches The Errors Assistants Sound Confident About

Generated prose fails in a characteristic way: it is fluent, complete, and occasionally wrong about a detail only an implementer would notice. A reviewer who reads for style will miss those errors, so documentation review must begin with claims rather than sentences. Check every promise, every default, every boundary condition, and every example against the behavior the change actually produces.

Assistants can support that discipline without replacing it. Ask the model to mark which statements come from the change and which come from its own general knowledge, then verify the second group by hand. Ask it to list every example in the passage and say what each one demonstrates, so a stale snippet appears as an item instead of hiding inside a paragraph. These prompts convert a vague sense of trust into a short, checkable set of obligations.

Tone deserves its own pass. Documentation accumulates a voice over years, and an assistant trained on generic material drifts toward marketing language unless the prompt anchors it to existing pages. Read the edited passage aloud next to its neighbors. If it sounds like a different author, the accuracy work is unfinished, because readers trust pages that sound like the rest of the manual.

Measurement Turns Documentation Into A Visible Deliverable

Documentation stays underfunded when its effects are invisible, so measure the things a team can act on. Track how long a stale passage survives after the behavior it describes changes, how often reviewers correct generated claims, and how many support questions disappear after an explanation is rewritten. Those numbers show whether the workflow is improving, and they justify the hours spent on prose.

Budget transparency helps too. Because usage-based billing reflects the counts a provider reports and prices cache reads and writes separately, a team can attribute the cost of a documentation pass to the feature that caused it, while members who invite new contributors can earn a referral commission when those contributors top up. None of that replaces craft; it simply keeps the craft accountable.

One habit anchors the rest: make the explanation a first-class artifact of the change. When the assistant drafts while the reasoning is live, the reviewer checks claims before style, the prompt names its sources, and the metrics reveal whether the prose held up, documentation stops decaying and starts keeping pace. Create a BestRelay account and run the next documentation pass alongside the code.