Which Model Should Write Your Documentation?
The best model for documentation is not necessarily the one that produces the smoothest prose. It is the one that turns your source material into accurate instructions with the least correction. Choose it by testing the work your team actually ships, then assign it a clearly bounded role.
1. Define the document before choosing the model
Start with the reader, their goal, and the evidence available. An onboarding guide needs a verified path to a first successful run. An API reference needs precise parameters and response behavior. A migration guide needs explicit differences between versions. These are different assignments, even when they describe the same service.
Write a short acceptance checklist before generating anything. For a quickstart, require prerequisites, installation, authentication, one working request, the expected result, and the most likely failure case.
2. Build a small, representative trial
Select three real tasks: updating an existing page after a code change, drafting a guide from implementation details, and explaining a confusing failure. Use material you can independently verify, not a topic where fluent writing could hide mistakes.
Give each candidate the same source bundle, instructions, and output limit. If you want a starting pair, compare Claude Sonnet 5.5 with GPT-6.1 Sol. Treat those as candidates to evaluate, not a predetermined ranking. Record the exact model identifier and test date so later comparisons remain meaningful.
3. Give the model authoritative inputs
Supply the relevant interface definitions, implementation excerpts, tests, configuration examples, and existing documentation conventions. Label which sources are authoritative. If a checked-in specification disagrees with deployed behavior, resolve that conflict rather than asking the model to guess.
Include a direct instruction: Do not invent flags, defaults, limits, response fields, or compatibility guarantees. Identify missing evidence explicitly. Ask for a separate list of unresolved questions alongside the draft. This makes uncertainty visible before an unsupported statement becomes published guidance.
4. Evaluate correctness before style
Review each draft against the same checklist. Keep correctness separate from readability: a polished page with an imaginary configuration option is still a failed page.
- Factual accuracy: Do claims match the supplied sources?
- Task completion: Can the intended reader finish the workflow?
- Executable examples: Do commands and requests work in a clean environment?
- Uncertainty handling: Does the draft flag gaps instead of filling them with guesses?
- Review effort: How much substantive editing does a maintainer need?
Record concrete defects and the time spent fixing them. A single overall score can conceal the exact failure that matters most to your team.
5. Test the examples outside the conversation
Run generated commands in a disposable environment. Validate request bodies against your schema, check referenced files, and verify that expected output matches actual behavior. Use test credentials with limited permissions, and never include production secrets in the prompt.
Where practical, move these checks into CI. Documentation examples can become smoke tests, while internal references can be checked against repository paths. Automated checks do not prove that an explanation is clear, but they catch failures that prose review often misses.
6. Compare the cost of accepted work
Measure the full workflow: source preparation, generation, retries, verification, and human editing. Token cost alone does not tell you which model is economical. A cheaper draft can become expensive if a maintainer must reconstruct every example.
CodeRelay Hub provides pay-as-you-go access through one endpoint at https://api.coderelayhub.com, usable from Claude Code, Codex, Gemini CLI, and any OpenAI SDK. Keep the prompt and evidence fixed when switching candidates, and confirm the selected model supports your required request features.
7. Delegate a scope, not ownership
Choose the model that performed best on your acceptance criteria, then document its approved role. You might allow first drafts and terminology cleanup while requiring explicit review for authentication, security, migrations, and destructive commands. Keep a human owner accountable for publication.
Repeat the trial when your source material, model version, or documentation workload changes. The durable decision is not a permanent model winner; it is a repeatable process for accepting reliable documentation.
Open a free workspace to run your first comparison.