The Tool Desk
Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Use AI to draft documentation, not to decide what the code does. Give it a bounded set of repository files, require evidence for each important claim, separate observations from inferences and unknowns, then verify the draft against implementation, tests and current authoritative references. A plausible explanation is not proof: HMRC describes AI “hallucinations” as output that appears sensible but is factually incorrect or made up (HMRC guidance).
Start with a bounded documentation task
Do not ask an AI tool to “document this repository” in one pass. Choose one module, component, workflow or behavior that a maintainer can review as a coherent unit. Provide the relevant source files and, where available, related tests, configuration, README material and recent changes. Narrow scope makes it easier to see which statements have evidence and which are guesses.
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Before sharing repository material, follow your organization’s data-handling rules. Exclude secrets and sensitive information, and check that the service and account you use are approved for the code. HMRC’s software guidance highlights reliable source data as well as security and privacy controls (HMRC guidance).
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Give the model sources it can rely on
Supply the code and project context that can support the requested explanation, rather than relying on the model’s general knowledge. GitHub recommends using material such as README files, documentation and recent pull requests as context, and specifying which sources the AI should trust (GitHub Copilot code review guidance).
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Ask it to cite a file path and symbol, test, or configuration key for each material claim. Require three distinct categories:
- Observed: what the supplied implementation, tests or configuration explicitly show.
- Inferred: a plausible interpretation that is not directly established by those files, with the basis for the inference stated.
- Unknown: behavior, intent or history the evidence does not resolve, plus what evidence could resolve it.
This format helps a reviewer locate support; it does not guarantee that the model will cite accurately or avoid invention. Treat the references as leads to check, not as verification.
Draft one useful unit at a time
Ask for a module summary, a function or class comment, a dependency-flow note, or a set of questions for maintainers. Keep each request small enough that a reviewer can compare the explanation with the relevant code. Do not let the model fill gaps in business rationale, historical intent or design motivation from intuition. Those claims need evidence such as requirements, tests, commit history or confirmation from someone who knows the system.
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Check whether the proposed text fits the project’s purpose, requirements and established design patterns. GitHub specifically stresses thorough review for legacy codebases and larger changes (GitHub Copilot code review guidance).
Verify claims against the implementation
For each important statement, open the cited code and confirm that it supports the wording. Be especially careful with statements about side effects, error handling, defaults, ordering, permissions, persistence and external calls: a short summary can omit the condition that changes the behavior.
- Use tests to check behavior where suitable, and run existing tests and static analysis when appropriate.
- Distinguish what static inspection suggests from what a test or command actually demonstrated. Do not describe behavior as tested unless the relevant test or result is available.
- Check that a claim is not contradicted by another implementation path, configuration setting or test.
- If the evidence is incomplete, narrow the statement or leave the behavior explicitly unknown.
GitHub recommends checking generated output against requirements and project design, not merely judging how convincing it sounds (GitHub Copilot code review guidance).
Check volatile technical details against current sources
Code documentation can mention API names, package versions, SDK behavior, security guidance or store policies that change over time. Do not treat AI output as authoritative for those details. Verify them against the current official documentation for the relevant library, platform or security authority. Microsoft warns that such technical facts may be out of date in AI-generated output (Microsoft technical guidance on AI-assisted development).
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A maintainer should check domain terminology, architecture, naming, assumptions and missing caveats before generated text becomes project documentation. A model may explain a branch of code while missing why that behavior matters, or it may turn a local implementation detail into a broader system claim. Preserve disagreements between sources rather than selecting the most confident explanation without evidence.
Keep unresolved behavior unresolved. State what is known, what is not established, and what could be checked next—for example, a missing test, a requirement, an older commit or a maintainer’s confirmation. HMRC advises that AI-enhanced software should support rather than replace human judgment and should allow people to correct errors or raise issues (HMRC guidance).
Keep the result traceable and maintained
Use the normal review and version-control process for the documentation change. Where appropriate, record material AI assistance and human review so the path from generated draft to accepted documentation is clear. The US government’s AI for the SDLC rulebook says AI-generated summaries and recommendations should be checked against authoritative sources and traced to delivered artifacts (AI for the SDLC rulebook).
Revisit the documentation when the code or the sources it relies on change. Documentation is not verified forever simply because it once matched a version of the implementation; HMRC’s software guidance also calls for version control, monitoring and timely updates (HMRC guidance).
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A prompt pattern for evidence-linked drafts
Use this as a starting point, adapting the file scope and output structure to the task:
Document only what can be supported by the files I provide. For each material statement, list the relevant file path and symbol or test. Separate directly observed behavior from inference. Do not infer business intent or historical rationale. Put unresolved questions in a separate list and state what evidence would resolve each one. Do not claim that behavior was tested unless a test or command result is supplied.
The prompt makes the evidence boundary explicit, but it is not a safeguard by itself. Check the cited files and claims independently before adopting the draft.
What published evidence can—and cannot—tell you
A 2024 study by Guelman, Leal, Xavier and Valente regenerated Javadocs for 23,850 Java methods and classes across three repositories using GPT-3.5 Turbo. In the authors’ assessment, 45.7% of generated comments were equivalent to the originals and 24.0% required minor changes, for a combined 69.7%; 22.4% were rated superior to the originals. The study also found that BLEU scores did not consistently track human judgments and could penalize comments judged better than the original (2024 study).
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11These results concern generated Java comments in a limited sample, with a particular model and task. They are not an accuracy rate for documenting legacy systems, other languages, whole repositories or current AI tools, and they do not remove the need to verify a draft.
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