How to Use AI to Understand a Codebase You Didn't Write

Start with structure, not code

Before asking an AI to explain any specific function, ask it to describe the overall shape of the repository: what the top-level directories are for, which files look like entry points, and which dependencies show up most often. This mirrors how an experienced engineer orients themselves, just faster.

A useful first prompt is to ask for a plain-language map of how a request flows through the system, from the entry point to the database and back. It will not be perfect, but it gives you a hypothesis to test against the actual code rather than starting from nothing.

Use it as a reading aid, not an oracle

AI is good at translating dense code into a paragraph you can skim, and at answering why a function exists by pattern-matching against similar code it has seen elsewhere. It is much less reliable at explaining decisions made for reasons not visible in the code itself, like a workaround for a vendor bug or a scar from a past incident.

Cross-check anything load-bearing against git history and commit messages, which often carry the actual reasoning that the code alone does not. Treat the AI's explanation as a first draft of understanding, not the final word.

• Ask for a directory-level map before diving into files
• Have it trace one request end to end as a starting hypothesis
• Ask it to summarize git blame or commit history on confusing code
• Verify anything critical by reading the actual implementation yourself

Where this pays off fastest

The biggest time savings show up on sprawling, loosely documented codebases where the alternative is grepping blindly for an hour. AI can narrow that search to a handful of candidate files in seconds, even if its explanation of what they do needs a second look.

It pays off less on small, well-organized codebases you could read in an afternoon anyway, where the overhead of prompting and verifying can cost more time than it saves.