Understanding AI-generated code

Understand only the code you need.

When AI-generated code breaks, what if a mentor already familiar with it could help? MENTOR helps users build understanding only where they need it, at the depth they need it, under their own direction.

Install the VS Code extension, sign in with Google, and jump straight to the change you actually want to inspect.

How MENTOR works

AI now produces more code than anyone can inspect at once.

Sometimes a piece of code becomes worth understanding: it is too important to trust blindly, it blocks the next prompt, or it looks fixable if you can just understand it. At that point, even deciding what to inspect is hard.

function explainTarget()

answer why

keep only the path

compare side branch

return next clue

function collectContext()

trace uploadPath

reuse guard branch

read cacheSummary

return focused scope

Q> why does this branch still stay?

A> check the guard call and the upload path together.

Stop looping for the next answer. Regain control by understanding.

Models are improving fast, and the amount of code they generate is growing just as fast. But when problems appear, builders cannot wait for a better model. They need to understand the generated code and resolve the issue themselves.

When AI writes most of the code, stabilization becomes the work that matters.

AI handles more and more of the work. Most of the time that is efficient. But when something breaks, developers can end up retrying, guessing, and hoping the next answer finally works.

problem appears

AI sometimes fails in ways we did not want.

Some topics are underlearned. Some tasks depend on private context that is hard to share. Some prompts simply carry too much context. In those cases, the result can drift.

Asking again instead of reading the code

ask again

Then we ask again instead of slowing down to read and steer.

When that pattern repeats, development starts to feel like a ritual. We keep asking for fresh code until something finally sticks, or the system drifts further away.

nobody knows

The hardest part is not knowing when the loop will end.

The problem is not a lack of generation. It is how hard it becomes to recover control once generated code starts to wobble.

Stabilization starts with understanding the code.

If developers understand what generated code is doing, they can steer the next prompt better, revise the code directly when needed, and turn imperfect output into useful signal.

better prompts

Understanding the code sharpens the next prompt.

The request stops being a vague retry and becomes a precise instruction grounded in actual control flow.

manual steering

If prompting is not enough, the code can be revised directly.

Understanding returns the developer to the operator's seat, where the system can be steered instead of waited on.

Imperfect output can still reveal a direction

useful hints

Even imperfect output can still reveal a path forward.

The goal is not one-shot perfection. The goal is reliable progress through understanding what each revision is trying to do.

MENTOR helps users build understanding on their own terms.

A general AI can explain too. But a small hallucination can derail the exact level of understanding the user needs. MENTOR combines AI explanation with call-aware structure so the user can verify and narrow the view with confidence.

user-led

The user stays in charge of where understanding begins.

MENTOR starts from the change the user cares about, then narrows the view around that decision.

right level

The graph keeps only the code needed for this question.

By keeping the relevant path recursively, MENTOR helps users prioritize what must be understood first.

grounded Q&A

Follow-up questions stay grounded in the traced path.

MENTOR searches the repository around that path and explains further with the user's context at the center.

Start now. We give you 20 50 credits.

When you need to understand AI-generated code, MENTOR is with you.

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