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.
You've asked Claude Code to build a PDF report generation feature. The initial implementation queries the database correctly, but the output has formatting issues: table columns are too narrow causing content truncation, dates display without proper formatting, and page break handling is incorrect. You've noticed these issues interact--changing column widths affects how dates render, and page breaks depend on content height.
What's the most effective approach for iterating toward a working solution?

• A. Start fresh with a detailed prompt specifying all formatting requirements upfront.
• B. Show Claude an example of a correctly formatted report and ask it to match that output, rather than listing the specific technical issues.
• C. Address the column width issue first with specific measurements, verify it works, then fix date formatting within the corrected columns, then adjust page breaks--testing after each change.
• D. Provide all three issues in a single detailed message with exact specifications for each, allowing Claude to address them together in one update.

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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 automated review calls the Claude API for each pull request, using tool_use with a report_findings tool that returns a JSON array of finding objects. Each object contains file_path, line_number, severity, category, and description. During testing on a large pull request touching more than 30 files, the response reaches the max_tokens limit and is truncated in the middle of the JSON, causing your pipeline's parser to fail.
What is the most effective way to handle this?

• A. Add retry logic that detects truncated JSON and resends the request with instructions to report only critical and high-severity findings.
• B. Split the review into multiple API calls that each analyze a subset of the changed files, and then merge the resulting findings arrays.
• C. Increase max_tokens to the model's maximum and instruct Claude to keep each finding description under 50 words.
• D. Switch from tool_use to prompting Claude to return findings as a Markdown list.

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During testing, you find that when a customer says "I need a refund for my recent purchase," the agent calls process _refund immediately - but populates the required order_id parameter with a plausible-looking but fabricated value instead of first calling lookup_order to retrieve the actual order ID. The refund call fails because the fabricated ID doesn't exist. Which change directly addresses the root cause of the agent fabricating the order_id value?

• A. Add server-side validation that checks whether the order_id exists in your database before executing the refund, returning an error to the agent if not found.
• B. Pre-parse incoming customer messages to extract any order IDs mentioned, and inject them into the conversation context before passing to Claude.
• C. Update the process_refund tool description to explicitly state that order_id must be obtained from a prior lookup_order call and must never be assumed or invented.
• D. Switch tool_choice from "auto" to "any" to force the agent to make a tool call on every turn.

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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.
Your extraction pipeline validates outputs against JSON schemas, but you need to implement human review given limited reviewer capacity (they can handle approximately 5% of total extraction volume).
What's the most effective basis for selecting which extractions to route for human review?

• A. Route extractions containing specific high-priority entity types (e.g., financial figures, dates) for human review, regardless of extraction confidence.
• B. Route extractions for review only when downstream systems report data quality issues or processing failures.
• C. Randomly sample 5% of extractions for review.
• D. Route extractions where the model indicates low confidence or where source documents contain ambiguous or contradictory information.

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What is the PRIMARY purpose of few-shot prompting?

• A. Demonstrate desired output patterns.
• B. Increase model size.
• C. Encrypt prompts.
• D. Reduce token costs.

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