To explain unfamiliar code with AI, give the model a precise question and a small evidence packet, then verify every claim against the repository. A useful explanation traces an entry point through calls, data transformations, state changes, side effects, and tests; it does not merely restate names in confident prose.

This is a reading workflow, not permission to edit. If you need a broader task loop, begin with the bounded workflow for using AI, then return here with one code path and one question.

Key Takeaways

Start with a question that has a clear boundary and a reason for being asked.

Build a minimal context packet from code, callers, types, tests, and repository instructions.

Separate observed facts, inferred behavior, and unresolved questions.

Trace state and side effects, not only function calls.

Keep file-and-line evidence so another reader can reproduce the explanation.

How do you explain unfamiliar code with AI without inventing the missing parts?

Use AI as a navigator that proposes where to look next, not as a substitute for looking. GitHub lists explaining code as a suitable coding-assistant task, while also telling users to understand, review, and validate generated work.［1］ The combination matters: explanation is useful precisely because it can be checked.

Begin by writing a reading question such as, “How does an authenticated request become a queued export job?” Avoid “Explain this repository,” which has no stopping point and encourages the model to blend unrelated modules. A bounded question tells you which entry point, state transition, output, and failure conditions belong in the answer.

Use three labels in your notes:

Label	Meaning	Acceptable evidence
Observed	Directly present in code, tests, or maintained docs	File, symbol, line, test, schema
Inferred	A conclusion assembled from several observations	Explicit reasoning plus all supporting locations
Unknown	Evidence is missing, ambiguous, generated, or environment-specific	A follow-up check or named owner

Do not allow the model to turn “unknown” into a plausible bridge. If a caller is outside the provided files, ask it to name the missing symbol or search query instead of guessing the caller’s behavior.

Step 1: Define the reading boundary and expected output

Write a short contract before sharing code:

Question: the behavior you need to understand.
Start: route, command, event handler, exported function, or public type.
End: response, persisted record, emitted event, file, or external call.
In scope: packages and files the explanation may inspect.
Out of scope: edits, generated directories, secrets, production data, and unrelated services.
Deliverable: a call map, state table, failure list, and evidence ledger.

This contract prevents a common failure: receiving a polished architecture essay that never answers the operational question. It also makes the result comparable with a human code review.

If your real goal is to change behavior, complete the reading pass first and then move to the separate AI coding workflow. Do not mix explanation and mutation in the same initial authorization.

Choose a useful slice

A slice should be large enough to include the behavior’s owner but small enough to follow in one sitting. Good slices include one HTTP route to its service and repository call, one CLI command to its file output, or one event consumer to its acknowledgment decision.

For a large repository, ask for a discovery list before file contents: likely entry points, symbol searches, configuration names, and tests. Review that list, then add files intentionally. A tool that can search the repository should still show which paths it inspected.

Step 2: Build a minimal and safe context packet

Include the entry point, directly called functions, relevant types or schemas, configuration defaults, and tests that express expected behavior. Add repository rules that affect the path, such as transaction ownership, authorization boundaries, or error-handling conventions.

Exclude credentials, environment files, customer records, private URLs, access tokens, and unrelated proprietary code. Replace production payloads with synthetic fixtures. OpenAI describes sandboxing and approvals as complementary controls: technical restrictions set where an agent can act, while approvals govern boundary crossings.［2］ The same idea improves a reading task even when no commands are run.

Ask the model to list the files it actually used. A claim based on a filename it never opened belongs under “unknown,” not “observed.”

Give types and tests priority over comments

Comments explain intent, but they may be stale. Public types, validation code, migrations, and executable tests often reveal the enforced contract. Read comments as claims that still need reconciliation with implementation.

When evidence disagrees, record the conflict instead of choosing the most convenient source. For example, a comment may promise retries while the caller treats the first error as terminal. The explanation should present both locations and identify which behavior executes today.

Step 3: Find the entry point and follow calls in execution order

Start where the system receives control. Follow direct calls in the order they can execute, including early returns, guards, error translation, and deferred cleanup. Do not jump immediately to the most interesting helper.

For each step, record:

input type and trusted/untrusted fields;
validation or authorization performed;
transformation and output type;
state read or written;
external effect or boundary crossing;
error behavior and caller response;
evidence location.

The diagram is a reading checklist, not a claim about any product or repository. Populate it only with facts from the code you inspected.

Ask relationship questions, not paraphrase questions

“What does this function do?” often produces a line-by-line restatement. Better questions expose contracts:

Who can call this function, and what has already been validated?
Which fields can change between entry and exit?
What invariant must hold before the external call?
Which errors are retried, translated, swallowed, or returned?
What cleanup runs on success, failure, cancellation, and timeout?
Which test would fail if this branch disappeared?

GitHub’s quickstart uses code explanation as a normal assistant task.［3］ Your improvement is to require traceable answers rather than accepting the first summary.

Step 4: Trace data, state, and side effects separately

A call graph alone is incomplete. Two functions may call each other correctly while sharing a cache, database row, lock, environment variable, or external queue in surprising ways.

Create three small ledgers:

Ledger	Questions
Data	Where was the value created, validated, normalized, and serialized?
State	Who owns it, when can it change, and what prevents conflicting updates?
Effects	What touches disk, database, network, subprocess, queue, or user-visible output?

For concurrent code, note lock ownership, transaction boundaries, cancellation, and whether a later observation can invalidate an earlier check. For asynchronous code, record who awaits a task, who receives failures, and what happens if the process stops between two effects.

This is where an AI explanation becomes useful for review. It can assemble repeated patterns across files, but you still verify every path and distinguish designed behavior from an accident of the current implementation.

Step 5: Extract invariants and failure paths

An invariant is a condition the code relies on across steps: an authenticated user owns the resource, a transaction remains open, an ID is unique, a file stays inside a root, or an approval matches the exact proposal being executed. State each invariant next to the code that establishes and consumes it.

Then ask for counterexamples. What if input is empty, duplicated, too large, stale, reordered, interrupted, or malicious? What if an external call succeeds but the local acknowledgment fails? What if cleanup itself errors?

If you are investigating an observed failure, switch to the evidence-led debugging workflow. Explanation maps the path; debugging must reproduce a failure and test competing causes.

NIST’s Secure Software Development Framework treats code review and analysis as parts of a broader secure development practice.［4］ That is a useful limit: a clear explanation supports review, but does not replace testing, threat analysis, or environment-specific verification.

Step 6: Verify the explanation against independent evidence

Review each sentence that claims behavior. Attach at least one code, test, schema, configuration, or maintained-document location. For an inference, attach every premise.

Use this verification sequence:

Reopen every cited symbol and confirm the quote-free paraphrase is accurate.
Search for alternate implementations, feature flags, and platform-specific branches.
Compare callers and tests with the stated preconditions.
Run a trusted, read-only or targeted test only if execution is authorized and helpful.
Ask a domain owner about unresolved policy, production, or historical intent.

Use the AI-generated code review checklist when the path touches authentication, persistence, subprocesses, parsing, network access, or destructive operations. An explanation that ignores these boundaries is not ready for a change decision.

Produce an explanation another reader can audit

The final note should contain:

the original question and explicit scope;
a five-to-ten-step execution narrative;
a data/state/effects table;
invariants and failure behavior;
evidence locations for every behavioral claim;
conflicting or stale documentation;
unknowns and the next safe check;
environments that were not verified.

Avoid copying large code blocks. Stable symbol names and concise observations are easier to maintain and reduce the chance that the explanation becomes a second, stale implementation.

When should you stop and ask for help?

Stop when the path requires credentials, production data, legal or policy interpretation, a missing private dependency, or a platform you cannot inspect. Also stop when the model repeatedly changes its story without new evidence.

For a vendor-specific terminal workflow, the Claude Code beginner guide can help with interface basics. Keep the evidence rules here regardless of the tool: tool access can improve discovery, but it does not make unsupported claims true.

Summary

Define one reading question with a start, end, and explicit exclusions.
Share a minimal, redacted packet of code, types, tests, and instructions.
Follow execution order, then trace data, state, and side effects separately.
Mark observations, inferences, and unknowns instead of blending them.
Verify every claim against repository evidence and record untested environments.

FAQ

Can AI understand an entire codebase at once?

It may index or search a large repository, but a reliable explanation still needs a bounded question and evidence trail. Break the system into paths whose entry, state, effects, and tests can be inspected.

Should I trust file and function names as explanations?

No. Names are clues, not executable evidence. Read implementations, callers, types, tests, and configuration before concluding what a symbol guarantees.

What code should I share with an external AI service?

Share only code your organization permits and only the minimum needed for the question. Remove credentials, personal data, private endpoints, customer payloads, and unrelated proprietary modules.

How do I know whether a comment is stale?

Compare it with current control flow, tests, schemas, configuration, and recent design records. If they disagree, record the disagreement and ask the responsible owner rather than silently choosing one.

Can an AI explanation replace code review?

No. It can accelerate navigation and summarize evidence, but reviewers still need to inspect the code, security boundaries, compatibility, tests, and affected environments.

Should the model run the code while explaining it?

Only when execution is authorized and a specific observation would resolve an uncertainty. Start read-only, review the command and its side effects, and keep execution evidence separate from the explanation.

What if two implementations handle the same interface?

List both and identify the selection mechanism: configuration, platform, dependency injection, feature flag, or runtime dispatch. Do not generalize behavior from one implementation to every environment.

How long should the final explanation be?

Use the shortest form that preserves the question, execution path, state, side effects, invariants, evidence, and unknowns. A concise auditable map is more useful than a broad architecture essay.

Further reading:

Disclaimer: This article provides general technical guidance. Follow your organization’s code-handling, security, licensing, and change-control rules, and involve qualified reviewers for consequential systems.

Sources:

GitHub Docs — Best practices for using GitHub Copilot — https://docs.github.com/en/copilot/get-started/best-practices
OpenAI — Running Codex safely at OpenAI — https://openai.com/index/running-codex-safely/
GitHub Docs — Quickstart for GitHub Copilot — https://docs.github.com/en/copilot/get-started/quickstart
NIST — Secure Software Development Framework — https://csrc.nist.gov/pubs/sp/800/218/final

Sources checked 24 August 2026.

How to Explain Unfamiliar Code with AI