Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsUse AI as a drafting assistant, not as the authority on how your legacy code works. Give it a narrow set of trusted repository files, require evidence for each important claim, separate observed behavior from inference and unanswered questions, then verify the draft against the implementation and have a maintainer review it.
Why AI-generated code documentation needs verification
A model can produce fluent, plausible explanations that are factually wrong or made up. HM Revenue & Customs describes this as a hallucination: output that appears to make sense but is incorrect or fabricated. That risk matters in legacy repositories, where a convincing explanation of business intent or runtime behavior can quietly become a misleading reference for future changes.
AI can still accelerate the work. 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. Human assessment rated 45.7% of the generated comments equivalent to the originals and 24.0% as needing minor changes, for 69.7% combined; 22.4% were rated superior to the originals. Those results apply to that model, language, sample, and comment-generation task—not to every language, repository, or whole-system explanation. The study also found that BLEU scores did not consistently align with human assessments.
A repeatable workflow for documenting legacy code with AI
1. Choose a small, verifiable scope
Start with one module, component, or behavior—not an open-ended request to explain the entire repository. Supply the relevant source files and, where available, related configuration, tests, README material, and recent changes. Keep secrets and sensitive data out of prompts, following your organization’s data-handling rules.
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2. Set an evidence boundary
Tell the model to use only the files you provide for claims about this project. Ask it to identify the file path and symbol, test, or configuration key that supports each material statement. Require distinct sections for directly observed behavior, plausible inference, and unanswered questions. This format makes claims easier to check; it does not guarantee that the model will avoid fabrication.
3. Draft one coherent unit at a time
Ask for a module summary, a function or class comment, a dependency-flow note, or a list of questions for a maintainer. Keep each draft small enough that a reviewer can compare it with the relevant code. Do not let the model fill in business intent or historical rationale from intuition: those claims need evidence such as requirements, tests, commit history, or confirmation from someone who knows the system.
4. Verify behavior against code and tests
Check each important behavior claim against the implementation. Inspect relevant tests, and run existing tests or static analysis where appropriate. Be precise about the evidence: a statement supported by reading code is not the same as a behavior observed in a test run. Do not let the documentation say a command or test was run unless you have its result.
Check whether the draft fits the project’s requirements, architecture, and established design patterns. GitHub’s guidance stresses thorough review, particularly for legacy codebases and larger changes. For current API names, package or SDK versions, and security guidance, consult current official references rather than relying on a model’s possibly stale knowledge.
5. Review uncertainty and omissions with a maintainer
Have a maintainer check names, architecture, domain meaning, and assumptions the code alone may not settle. Leave unresolved behavior explicitly marked as unknown, and record what evidence or investigation could resolve it. If sources disagree, document the disagreement rather than selecting whichever explanation sounds most confident. Human oversight is necessary because the model cannot supply missing project knowledge reliably.
6. Keep the result auditable and current
Use your normal documentation and change-review workflow to record material AI assistance and human review when appropriate. Verify AI-generated summaries and recommendations against authoritative sources, and preserve traceability from AI use to the delivered artifact. Keep documentation under version control and revisit it when the code or relevant sources change.
A prompt pattern that makes claims easier to check
Adapt this prompt to the files you are actually providing:
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.
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This is a review aid, not a safeguard that makes every response accurate. Check the resulting statements independently against the repository.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to choose an AI workflow or tool
No particular product is necessary to follow this method. If you are evaluating tools, compare the capabilities that affect verification and safe use:
- Source grounding: Can you provide relevant repository files and tell the model which material to trust?
- Traceability: Can reviewers connect claims to paths, symbols, tests, or other evidence?
- Language and format support: Does the workflow handle the code and documentation formats your project uses?
- Review and export: Can maintainers inspect, edit, and move the draft into the project’s normal review process?
- Access and data handling: Do access controls and data terms meet your organization’s rules for source code and sensitive information?
Evaluate these against your own repository and policies; feature availability and data-handling terms vary by service and plan.
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