Production monitoring shows that follow-up queries like "summarize what we learned about market trends" consistently take 40+ seconds. Investigation reveals the coordinator spawns the synthesis subagent for each summarization request, passing 80K+ tokens of accumulated findings. The coordinator already has these findings in its context from orchestrating the research.
What's the most effective way to improve response time for these follow-up summaries?

• A. Have the coordinator handle straightforward summarization requests directly using its existing context, reserving subagent spawning for complex analysis.
• B. Enable prompt caching on the synthesis subagent to reduce the overhead of repeatedly transferring the same research findings.
• C. Pre-generate and cache summaries at multiple granularities whenever new findings accumulate.
• D. Spawn the synthesis subagent with reduced context and have it request specific findings from the coordinator on-demand.

Your update_user_profile tool accepts a user_id (required) and an optional fields_to_update object. In testing, Claude frequently omits user_id or passes incorrectly structured data. What is most critical for helping Claude understand what parameter values to provide?

• A. Detailed error responses explaining why invalid parameter values were rejected
• B. Strict JSON Schema type constraints marking user_id as required and defining fields_to_update as an object type
• C. Verbose parameter names encoding format hints, such as user_id_string_uuid_format
• D. Clear parameter descriptions explaining expected format, such as "user_id: UUID of the user to update (required)"

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.
After deploying automated code review, developers report that approximately 35% of findings are false positives following consistent patterns: style suggestions that contradict team conventions, security warnings for patterns that are safe in the deployment environment, and performance suggestions that would degrade this particular use case.
You want to reduce false positives while enabling the model to generalize its judgment to novel code patterns it has not seen before.
Which approach is most effective?

• A. Create a comprehensive specification of every pattern that must not be flagged and include the complete document in the system prompt.
• B. Include few-shot examples containing annotated code snippets that distinguish acceptable project patterns from genuine issues in each category.
• C. Add general instructions telling Claude to be conservative and report only definite issues.
• D. Use keyword-based post-processing to remove findings containing terms such as "convention,"
  "context-dependent," or "trade-off."

A team wants to reduce hallucinations without significantly changing application logic. Which improvement should be implemented FIRST?

• A. Shorten all outputs.
• B. Add reliable retrieval sources.
• C. Remove role instructions.
• D. Raise temperature.

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 includes a release-notes generation step that classifies and summarizes approximately 200 commits at the end of each weekly release cycle. Each commit is currently sent as a separate Messages API request using a Sonnet-tier Claude model. The release notes are not needed until the following morning, providing approximately 12 hours of acceptable latency.
Your team must reduce the per-token API cost while retaining the same model, prompts, and output quality.
Which approach satisfies all these constraints?

• A. Concatenate all 200 commit messages into one Messages API request because reducing the number of requests always reduces token costs.
• B. Replace the Sonnet-tier model with a Haiku-tier model to obtain a lower per-token price.
• C. Submit the 200 requests through the Message Batches API with unique custom_id values and retrieve the results after the batch finishes.
• D. Issue the 200 Messages API requests concurrently because parallel execution reduces the per- token price.