You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
Your team has three requirements for Claude Code's behavior in your project
1. Claude must never modify files in the db/migrations/ directory
2. Claude should prefer your custom logging module over console.log
3. All TypeScript files must be auto-formatted with Prettier after
every edit
All three are currently written as instructions in your project's CLAUDE.md. During a complex refactoring session, a developer discovers that Claude edited a migration file, violating requirement #1. How should you restructure these requirements across Claude Code's configuration mechanisms?

• A. Move all three requirements into .claude/rules/ as path-scoped rules: one targeting db/migrations/** that forbids editing those files, and others targeting **/*.ts for the logging convention and formatting instruction.
• B. Rewrite all three requirements in CLAUDE.md using stronger directive language and add few- shot examples that demonstrate Claude refusing to edit migration files and running Prettier after edits.
• C. Configure hooks for all three: a PreToolUse hook script that blocks Edit calls targeting db/migrations/,a PreToolUse hook script that adds logging convention context before edits, and a PostToolUse hook that runs Prettier after TypeScript edits.
• D. Add Edit(./db/migrations/**) to permissions.deny in the project settings, keep the logging preference in CLAUDE.md, and add a PostToolUse hook on the Edit tool that runs Prettier on changed TypeScript files.

Which prompt revision is MOST likely to improve output quality?

• A. "Summarize this."
• B. Use only uppercase letters.
• C. Include objective, context, constraints, and expected format.
• D. "Help me."

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

After 30+ turns, your conversational assistant shows noticeably slower responses and occasionally produces less coherent outputs. Investigation reveals: (1) average conversations reach 50,000 tokens by turn 35, (2) production logs show 94% of user messages only reference the previous 3-5 exchanges, (3) the 6% of queries referencing earlier context typically ask about information the user could easily re-state. Your goal is to improve response speed and quality while maintaining good user experience. What's the most effective approach?

• A. Enable prompt caching and continue sending the complete conversation history, using cached prefixes to reduce per-request costs while preserving all context.
• B. Implement a sliding window keeping only the system prompt and last 8-10 turns. When users reference earlier context, acknowledge the limitation and ask them to re-state the relevant information.
• C. Implement a summarization layer that progressively compresses older conversation turns into a running summary while keeping the most recent 5-6 turns verbatim, maintaining full historical context in condensed form.
• D. Build a retrieval system that stores all conversation turns and uses semantic search to pull in relevant historical context only when the current query appears to reference past information.