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Building AI-Native Workflows1 / 2

1 min lesson

Evaluate retrieval on its own

Take this situation: "Your agent keeps producing incorrect edits on a large repo. Before you touch the prompt or swap models, what should you check first and why?" Lead with your decision, then add the reason.

Step 1 of 2

Evaluate retrieval on its owndon’t blame the model for a bad fetch

If you only measure final edits, you can’t tell whether a bad result came from the model or from never giving it the right file. Score retrieval independently before you judge generation.

  • Build a set of queries with the chunks a human says should come back.
  • Measure recall: did the must-have files land in the top-k results?
  • Track precision so you’re not flooding the window with near-misses that cost tokens.
  • When generation is wrong, check the retrieved set first - often the file the model needed simply wasn’t there.
Cursor’s indexing is the reference

Cursor indexes the codebase so the editor and Agent can pull relevant files into context automatically.

When you deploy a workflow, you’re building on that retrieval layer, so “did we retrieve the right code” is a question you can answer with its index, not one you reinvent.

Interview move

If a design question reaches “the agent gives wrong answers on a big repo,” split the problem out loud: is retrieval missing the file or is generation fumbling a file it had?

Proposing a retrieval eval before touching the prompt reads as production experience.