Use case
How to Review AI-Generated Code Safely
Use a contract-first review loop to inspect AI-generated code, test risky boundaries, and decide whether a proposed change is ready to merge.
Explore the use case →Developer use cases
Practical ways to verify AI-assisted code work
Start with the situation in front of you, then use a clear evidence loop for reviewing proposed code, debugging a plausible fix, tracing unfamiliar state, or preparing a focused review. Each resource gives a direct answer and a small practice loop without treating a single session as a verdict.
Use case
How to Debug a Plausible AI Fix
A disciplined way to investigate a believable AI-suggested fix without confusing a fluent explanation with evidence about the real failure.
Explore the use case →Use case
How to Validate Copilot Suggestions
Validate autocomplete and Copilot suggestions by checking their contract, assumptions, and tests before a small completion becomes a hidden behavior change.
Explore the use case →Use case
How to Spot Hallucinated APIs in Code
Find invented or mismatched APIs in proposed code by checking imports, installed versions, signatures, and failure behavior instead of trusting familiar names.
Explore the use case →Use case
How to Trace State in Unfamiliar Code
Trace state in an unfamiliar function by following only the values, references, branches, and side effects that can change the behavior you are investigating.
Explore the use case →Use case
How to Regain Useful Code Recall
Regain useful code recall with small retrieval exercises that target recurring patterns, then compare your draft with a trusted reference and name the gap.
Explore the use case →Use case
How to Prepare for a Focused Code Review
Prepare a small, evidence-rich code review by clarifying the contract, reducing unrelated changes, and showing tests that cover the risky behavior.
Explore the use case →Use case
How to Investigate a Production Bug
Investigate a production bug with a safe evidence loop: stabilize impact, capture facts, form narrow hypotheses, and verify the smallest corrective change.
Explore the use case →Use case
How to Understand an AI-Generated Pull Request
Understand an AI-generated pull request by mapping the request to the diff, tracing its data flow, and isolating every assumption before approval.
Explore the use case →Use case
How to Test an AI-Suggested Refactor
Test an AI-suggested refactor by preserving observable behavior, specifying invariants, and comparing before-and-after cases instead of trusting cleaner-looking code.
Explore the use case →Use case
How to Practice Before an On-Call Shift
Prepare for on-call with short, repeatable debugging and tracing drills that strengthen evidence habits without pretending to simulate every incident.
Explore the use case →Use case
How to Inspect an Autocomplete Completion
Inspect an autocomplete completion by checking the surrounding contract, its hidden defaults, and the smallest test that shows whether the proposed lines belong there.
Explore the use case →Check a focused starting point
One diagnostic session is a snapshot, not causal proof of why a result occurred. It can help you choose a focused next practice step without making a broader claim about your ability.
Take the free diagnostic