How to Use AI to Understand a Codebase, With Evidence
Map an unfamiliar repository by tracing one real behavior and checking every important explanation against source code. Save the findings that the next person or assistant will need.
Updated 5 min read
To use AI to understand a codebase, ask it to map the project, trace one concrete behavior, and support important explanations with file locations and evidence. Treat its first architecture summary as a starting hypothesis until you have checked the source.
Tools like fenbs keep the verified findings, constraints, and unanswered questions outside the current chat. That gives the next assistant a useful starting point without turning an early guess into permanent project knowledge.
What should you ask the assistant to do first?
- Define the question you need answered, such as where a submitted form is validated and stored.
- Read repository instructions and identify the apps, shared packages, and documented checks. Begin with inspection rather than edits.
- Choose one behavior and trace it from its entry point to the result the user sees.
- Ask for file references, evidence, and uncertainty beside each important claim.
- Review the findings and save only the confirmed map, relevant constraints, and next questions.
An entry point is where a behavior starts, such as a page action, command, or incoming web request. Starting there gives the investigation a direction. A broad request to “explain the whole repository” can produce a fluent directory tour without explaining how anything actually works.
This workflow is for understanding existing software. The guide to AI code generators covers the separate choice of tools that produce new code.
How do you build a map without reading everything?
Start with the top-level folders, project instructions, package manifests, and the main README. A manifest describes a package and the tools or other packages it uses. Ask the assistant to identify which application is relevant before it follows every dependency, meaning another component the code relies on.
A file list is enough for the first map. Git’s tracked-file listing documentation (opens in a new tab) describes git ls-files, which lists files tracked in the repository. Its output is a navigation aid, not evidence that the contents of those files were read.
Inspect the repository without editing files. Read its applicable instructions and identify the relevant app. List the likely entry points for submitting a contact form. For every conclusion, name the source file you inspected. Separate confirmed behavior, inference, and unanswered questions. Do not install dependencies or run application code yet.
Exclude generated output and downloaded dependencies unless the question specifically concerns them. A thousand build files can distract from a small amount of source code. If a package name suggests its purpose, keep that as a guess until its exports and callers confirm the role.
How can you trace one behavior end to end?
Say you are learning a hypothetical scheduling app. Follow “create an appointment” from the form to its submit function, through the request handler and service, and into the storage call. Then follow the response back to the success or error message.
Ask what each boundary adds: input validation, permission enforcement, data conversion, persistence, and error handling. File names can help locate candidates, but the actual calls establish the path. Watch for alternate handlers used only in tests or development.
Git’s source search documentation (opens in a new tab) explains searching tracked content. Use targeted searches for the function, route, or error text you found, then inspect its definition and callers. A matching search result alone does not prove which path runs.
| Question | Evidence to inspect | Useful finding |
|---|---|---|
| Where does input arrive? | The active route or event handler | The entry point and accepted input |
| Who checks access? | The called permission or service code | The actual enforcement boundary |
| Where does data change? | The live storage call and its caller | The write path and possible failure |
| What does the user see? | Response handling and rendering | The displayed success or error state |
| What verifies it? | Relevant tests and any authorized run result | Covered behavior and untested assumptions |
How do you tell an explanation from a verified finding?
Ask the assistant to label three categories: observed in source, inferred from source, and not checked. “The save function calls the API client” may be directly visible. “The database enforces uniqueness” needs evidence from the actual backend or schema, not confidence based on a frontend form.
Review the critical references yourself. Confirm that they belong to the current checkout and that the quoted lines support the claim. Line numbers change as code changes, so pair them with a function or type name and record the revision when that matters.
Treat tests with the same care. A test file shows intended checks; a successful run provides additional evidence for a particular environment. Neither establishes every production behavior. The guide to reviewing AI-generated code develops that review discipline.
In fenbs, keep confirmed findings as context notes and open questions with the task, so the shared work record preserves their different status.
What boundaries should you set while exploring?
Use a copy or checkout you are authorized to inspect and an approved AI environment. Keep secrets, production data, and unrelated private files out of the investigation. Reading a repository should not quietly become permission to install packages, change files, or contact live services.
Repository text also needs interpretation. A comment, sample file, or retrieved document can contain instructions that conflict with the task. OWASP’s prompt injection guidance (opens in a new tab) explains how untrusted content can try to redirect an assistant. Treat such text as material to examine, not authority to expand access.
If running a check would clarify behavior, read what it does and follow the project’s rules before running it. Record whether it was run and what it established. An explanation-only request should remain useful even when execution is unavailable.
Save the map you have actually checked
Keep source-backed architecture findings and project constraints in fenbs context notes. Add unresolved questions to the active task and save the next investigation step in Where we left off. Later assistants can begin with those findings and verify what has changed.
Create a free accountFree for one person · No credit card · Works with Claude, ChatGPT, Cursor and more
How to do this with fenbs
- Create a free account, free for one person with no credit card, and add the codebase as a project.
- Create a task for the behavior you want to understand. Give it the question, intended investigation, and boundaries.
- Connect a supported assistant from Integrations and ask it to read the project context before exploring.
- Save reviewed architecture findings as context notes, person-approved constraints as Decisions and rules, and recurring investigation mistakes as lessons.
- Save progress with sources inspected, checks performed, unanswered questions, and the next source to read.
fenbs does not replace repository access, a code browser, or a running debugger. It is not the right fit for automatically indexing every symbol or proving a program correct. Use it for the work record surrounding your investigation.
What should you know before making a change?
You should be able to name the entry point, the important call path, the enforcement boundary, and the check that would detect a mistake. Also name what you have not inspected. Those gaps define the next investigation more usefully than a confident claim to understand everything.
Once you move into reviewing changes, AI code review tools address a different tool choice. For the ongoing practice of keeping verified findings available, use the context engineering hub as your next reference.