AI Code Explanation Tools: What Good Looks Like and How to Test

Asking an AI to explain code is now one of the most common things developers do with it. Paste a function, get a paragraph. Point a tool at a repository, get an overview. The results range from genuinely clarifying to fluent restatements of what the code obviously says, with the occasional confident description of behavior that does not exist.

The difference between a useful explanation and a useless one is not mostly about the model. It is about what the tool knows, how it structures the answer, and whether you can check it. This post describes what good AI code explanation looks like, the failure modes to watch for, and a practical way to evaluate tools on your own code before you rely on them for onboarding, reviews, or incident work.

Key takeaways

• A good explanation answers why and how in terms of the system, not just what each line does. It connects the code to its callers, data, and purpose.
• Grounding is non-negotiable: every claim should point to a file and line you can open.
• Scope matters. Line-level, function-level, flow-level, and architecture-level explanations need different context and different formats.
• The best tools combine prose with structure: code excerpts, numbered steps, call flows, and diagrams that match the text.
• Evaluate tools on questions you already know the answer to, from your own repository, and score accuracy, grounding, completeness, and honesty about uncertainty.

What developers actually need explained

"Explain this code" covers several different questions, and a tool that handles one well may fail at another.

Level	Typical question	What a good answer needs
Line or expression	What does this regex or bit trick do?	Precise semantics, edge cases
Function	What does reconcileLedger do and when would it fail?	Inputs, outputs, side effects, error paths
Flow	What happens when a user clicks Checkout?	Entry point, sequence across files, async steps, storage
Subsystem	How does billing work?	Main components, boundaries, data model, key flows
Architecture	How is this repo organized?	Modules, dependencies, entry points, where to start

Line and function explanations can be done with the code in front of the model. Flow, subsystem, and architecture explanations need knowledge of relationships across many files. That is where tools differ most.

What a good explanation looks like

It starts with purpose

"reconcileLedger compares the internal ledger with the payment provider's settlement report each night and flags mismatches for finance review" is more useful than "This function takes two arrays and loops over them". Purpose is what a reader lacks; the loop they can see.

It follows the real path

For a flow question, a good answer is a sequence:

1. The Checkout button calls submitOrder in web/checkout/actions.ts:22.
2. submitOrder posts to /api/orders, handled by api/routes/orders.ts:40.
3. The handler validates with OrderSchema, then calls OrderService.create in api/orders/service.ts:88.
4. create writes the order with status pending and enqueues a charge-order job.
5. The worker in worker/jobs/charge-order.ts:15 charges the card and updates status to paid or failed.

Numbered, anchored, and in execution order. Each step can be checked in seconds. If you have ever traced a request through a web app by hand, this is the map you were building.

It names the non-obvious

The parts worth explaining are the ones a reader would miss: the async hop through a queue, the retry policy, the feature flag that changes the path, the cache that makes a write appear delayed. A good explanation spends its words there.

It shows evidence

Short code excerpts next to the relevant step beat long blocks of prose. A flowchart or call diagram that matches the text helps readers hold the sequence in their head.

It admits uncertainty

"The job is registered dynamically in worker/registry.ts; I could not confirm which queue it runs on" is a good sentence. A tool that never says anything like it is probably filling gaps with guesses.

Failure modes to watch for

Restating the code. "This function checks if the user is null, and if so, returns an error." The reader could see that. Common when the tool only has the snippet and no context.

Plausible invention. Describing a retry mechanism, a validation step, or a caller that does not exist, because code like this usually has one. The same root cause as agents hallucinating APIs: the tool wrote from patterns instead of from your code.

Missing the indirect path. Explaining the synchronous part of a flow and stopping at the event emit, because the listener is not connected by a direct reference.

No anchors. Explanations that mention "the order service" without a path force you to search, and make it impossible to tell whether the tool looked or guessed.

Wrong level. A line-by-line walkthrough when you asked how the subsystem fits together, or a vague architecture summary when you asked about one function.

Stale knowledge. Explaining code as of an old index after a refactor.

What makes tools better or worse

Context source

Tools that only see the pasted snippet or the open file can explain lines and functions. Tools that index the repository can explain flows, but how they index matters. Text chunks and embeddings find similar code; a parsed code graph knows definitions, callers, and imports. For flow and architecture questions, the latter is far more reliable. The tradeoffs are covered in RAG for code: the limits.

Structure of the output

Wall-of-text answers are hard to verify. Look for numbered steps, code excerpts tied to steps, diagrams, and links into the source.

Interaction

Explanations raise questions. A tool that keeps the context of the previous answer for follow-ups ("what happens if the charge fails?") saves a lot of re-asking.

Privacy and data handling

Explaining private code means the tool sees private code. Ask where indexing happens, whether source leaves your machine, what is stored, and for how long. See local-first code indexing and privacy.

How to evaluate a tool on your own code

Do not evaluate on toy repositories. Use your own, and use questions an expert on your team can answer.

Build a question set

Pick 10 to 15 questions across levels:

Function
1. What does <function> do, and what are its failure modes?
2. What are the side effects of <function>?

Flow
3. What happens end to end when <user action>?
4. How does <async job> get triggered, and what happens if it fails?
5. Where is <permission or auth check> enforced for <endpoint>?

Subsystem
6. How does <subsystem> work? What are its main parts?
7. What data does <subsystem> own, and who else reads it?

Architecture
8. What are the main modules in this repo and how do they depend on each other?
9. Where would I add <a typical new feature>?

Traps
10. A question about a feature that does not exist.
11. A question where the answer depends on a feature flag or config.

The trap questions are important. Question 10 tests whether the tool will invent an answer.

Score each answer

Dimension	Question to ask	Scale
Accuracy	Is everything stated true?	Count of false claims
Grounding	Are claims anchored to real files and lines?	None, some, all
Completeness	Did it cover the steps an expert would list?	Missing steps
Relevance	Did it spend words on what matters?	Low, medium, high
Honesty	Did it flag uncertainty or say "not found"?	Yes or no
Usability	Could a new engineer act on it?	Low, medium, high

Have the expert score blind where possible. A single false claim in a flow explanation can be worse than an incomplete answer, because readers act on it.

Try follow-ups

For two or three flows, ask follow-ups: "what if the payment fails?", "where is that retried?", "show me the test for that". Good tools keep context and stay grounded. Weak ones drift.

Check freshness

Make a small change (rename a function, move a file) and ask about it. Does the tool reflect the change, and does it tell you what revision it is reading?

Using explanations well

Even a good tool works better with good habits:

• Ask specific questions. "How is a refund issued from the admin panel?" beats "explain refunds". See asking better questions about code.
• Open the anchors. Spot-check two or three references per answer. If they are right, trust grows. If not, you have learned something important about the tool.
• Use explanations as a map, not a substitute. The explanation gets you to the right files quickly. Reading those files is still the job.
• Write down what you learn. A good explanation of a flow is worth keeping near the code for the next person.

Where OpenVisio fits

OpenVisio is built around grounded explanation. In Ask, a question about a repository gets a scrolling answer with a numbered process, and a sticky panel shows the relevant code, a flowchart, or the 3D architecture for the section in view, with follow-up questions docked under the answer. The Tour offers a guided walkthrough of the codebase in article, code walk, diagram, and slides views. Indexing runs in the browser, so source does not leave your machine. Ask and the AI narration are part of OpenVisio Pro; the map, Tour script, and views are free.

Whatever tool you use, judge it by one standard: after reading the explanation, can you open the code and find that it said what is actually there? If yes, it is saving you time. If not, it is costing you more than it saves.