You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The…

Question

You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.

Your pipeline reviews every PR using a single API call with a static prompt containing the diff and full text of each changed file – unchanged files are not included. Reviews are posted asynchronously and don't block PR creation. Developers report that reviews consistently miss bugs involving cross-file interactions – for example, a PR renames a function's parameters but the review doesn't flag callers in unchanged files that still use the old argument order. Evaluation shows cross-file bugs account for 35% of production incidents from reviewed PRs.

What is the most effective change to your review design?

Accepted Answer

A. Redesign the review as a turn-limited agentic task where the model can read files and search the codebase via tools, following references to verify cross-file findings.

Answer

B. Run parallel review passes per changed file with direct dependents included in each pass, then aggregate and deduplicate findings using a final summarization call.

Answer

C. Add chain-of-thought instructions asking the model to list all external references in the diff, then reason step-by-step about how each change might affect callers in other files.

Answer

D. Use static analysis to build a dependency graph of changed code, then expand the prompt to include all files within two dependency hops of any changed file.

Question 118