What this prompt does
This prompt builds a systematic Q&A session for understanding a codebase you just inherited. You set [project_type], [tech_stack], and [codebase_size], and the AI generates structured questions for Cursor chat that move from high-level architecture down to a single feature traced end-to-end. The seven steps cover entry points and route definitions, tracing [feature_name] from user input through the API layer and business logic to the database and back, the authentication and authorization model, error-handling patterns, the service or dependency-injection layer, and configuration plus environment-variable usage.
The structure works because it onboards you the way an experienced engineer actually does: top-down for orientation, then depth-first on one real feature. Tracing [feature_name] from input to response forces you to learn the actual data flow rather than skimming files at random. Crucially, each generated question comes paired with the @-mention context — a file, folder, or symbol — you should attach in Cursor chat, because the same question gets a far better, more grounded answer when the right context is included. That pairing is what separates a vague answer from one that cites real code. By the end you have walked the architecture, the auth model, and the error and service layers in a deliberate order instead of poking around hoping something makes sense.
When to use it
- You just joined a project and need to be productive before touching anything
- You inherited a client codebase with little or no documentation to lean on
- You need to trace one feature end-to-end before extending or modifying it
- You want to understand the auth and authorization model before changing protected routes
- The service or dependency-injection layer is unclear and you need it mapped explicitly
- You are evaluating an unfamiliar repo and want a fast, structured read of its architecture
Example output
You get a grouped list of questions: architecture first (entry point, routing, where data flow begins), then an end-to-end trace of [feature_name], then auth, error handling, the service layer, and configuration. Each question is annotated with the @-mention — file, folder, or symbol — to attach in Cursor for the best answer. It reads like an onboarding script you work through in order rather than a loose pile of questions.
Pro tips
- Pick a
[feature_name]that touches many layers (billing, authentication) so the end-to-end trace teaches you the most in one pass - Be accurate with
[tech_stack]— naming Celery and Redis, for example, prompts better questions about background jobs and queues - Use the suggested
@-mentions; asking the same question without context usually yields a vaguer, less grounded answer - Work the questions in order — the architecture context makes the later feature-trace answers noticeably sharper
- When an answer is thin, re-ask with a more specific
@-mention rather than just rephrasing the question - Save the strongest answers as notes; they become your first informal architecture document for the project
