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.
The automated review consistently flags patterns your team uses intentionally--force-unwrapping optionals in test files, using large coordinator classes that follow your established architecture, and importing internally maintained modules marked as deprecated in the public SDK.
Developers dismiss approximately 30% of all findings as project-specific false positives.
Which approach prevents the model from generating these findings in the first place by supplying the project's conventions as persistent context during every review?

• A. Document the team's accepted patterns and intentional conventions in the project's CLAUDE.md file so the model receives this context during every review.
• B. Have developers add inline suppression comments at flagged lines and preprocess diffs to exclude suppressed lines before sending code to the model.
• C. Configure the review to analyze only the changed lines in the diff without the surrounding file context, reducing the amount of code the model evaluates.
• D. Build post-processing keyword filters that suppress findings containing terms such as "force unwrap," "large class," or "deprecated import" before results reach developers.

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Your MCP server includes archive_file(file_id) and delete_file(file_id) tools. Production logs show the agent calls delete_file when users ask to "remove old backups," but company policy requires archiving backup files. Both tools currently have minimal descriptions: "Archives a file" and
"Deletes a file." Which change most directly improves tool selection for this scenario?

• A. Expand tool descriptions to clarify use cases, adding guidance like "Do not use for backup files" to delete_file.
• B. Add few-shot examples to the system prompt demonstrating that requests involving "backup" or
  "old" should use archive_file.
• C. Add a confirmation step that requires users to type "CONFIRM DELETE" before delete_file executes.
• D. Implement server-side validation that rejects delete_file calls for files tagged as backups, returning an error message suggesting archive_file.

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You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
The system routes documents with extraction confidence below 85% to human review. A quarterly audit reveals that 12% of high-confidence extractions (85%) also contain errors--cases where the model finds plausible-but-incorrect values. Error sources vary: comparison tables showing competitor specs, appendices referencing different product variants, and ambiguous phrasing the model misinterprets. You need a sustainable strategy to catch these high-confidence errors and measure whether improvements reduce the error rate over time.
What approach is most effective?

• A. Implement heuristic rules that flag documents containing comparison tables or appendices for review regardless of confidence score.
• B. Lower the confidence threshold from 85% to 70%, routing a larger volume of extractions to human review.
• C. Implement stratified random sampling reviewing a fixed percentage of high-confidence extractions weekly, enabling error rate measurement and novel pattern detection.
• D. Add a verification pass that re-extracts from each high-confidence document, flagging cases where the two extraction attempts produce different results.

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The web search agent has gathered several relevant sources for a research topic. The document analysis agent now needs to examine these sources. How does information typically flow between these two specialized subagents?

• A. The coordinator agent receives the web search agent's output and includes relevant findings in the prompt when invoking the document analysis agent.
• B. Both agents access a shared memory store where the web search agent writes findings and the document analysis agent reads them.
• C. The agents communicate through an event-driven message queue, with the document analysis agent subscribing to web search completion events.
• D. The web search agent directly invokes the document analysis agent, passing the discovered sources as parameters.

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Users report that final reports sometimes lack depth on specific subtopics. Investigation shows that the document analysis agent frequently identifies gaps - for instance, noting "the retrieved sources discuss API authentication but lack details on token refresh patterns" - but under the current strict pipeline, this insight isn't actionable since search has already completed. What's the most effective architectural change?

• A. Have the synthesis agent attach confidence scores to each section and flag areas with insufficient coverage for manual review.
• B. Have the analysis agent report specific gaps to the coordinator, which triggers targeted searches and re-invokes analysis until sufficient.
• C. Add a research planning agent before the search phase that decomposes topics into specific sub- questions.
• D. Have the coordinator review analysis output for gap indicators and re-invoke search with gap- informed queries when gaps are detected.

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