Domain 4: Prompt Engineering and Structured Output (20%)

4.1 Designing Prompts with Explicit Criteria to Improve Accuracy

Key knowledge:

• Explicit criteria are more effective than vague instructions (e.g., “flag comments only when they contradict code” vs “check comment accuracy”)
• Generic guidance like “be more conservative” works worse than concrete categorical criteria
• The effect of false positives on developer trust: high false-positive rates in some categories undermine trust in accurate categories

Key skills:

• Define review criteria: what to report (bugs, security) vs what to ignore (minor style)
• Temporarily disable categories with high false-positive rates
• Define explicit severity criteria with code examples for each level

4.2 Using Few-shot Prompting to Improve Output Consistency

Key knowledge:

• Few-shot examples are the most effective method for producing consistently formatted, actionable output
• Few-shot can demonstrate handling of ambiguous cases (tool selection, gaps in test coverage)
• Few-shot helps the model generalize to new patterns rather than just repeating defaults
• Few-shot can reduce hallucinations in extraction tasks

Key skills:

• Provide 2–4 targeted examples for ambiguous scenarios with rationale
• Include few-shot examples that demonstrate the output format (location, issue, severity, suggested fix)
• Provide examples that distinguish acceptable code patterns from real issues
• Provide examples of correct extraction from documents with different structures

4.3 Enforcing Structured Output with tool_use and JSON Schemas

Key knowledge:

• tool_use with JSON Schemas is the most reliable way to guarantee schema-conformant output and eliminate JSON syntax errors
• With tool_choice: "auto" the model can return text; with "any" it must call a tool; forced selection chooses a specific tool
• Strict JSON Schemas eliminate syntax errors but do not prevent semantic errors (totals don’t add up; values in wrong fields)
• Schema design: required vs optional fields; enums with “other” plus a detail string for extensibility

Key skills:

• Define extraction tools with JSON Schemas and parse data from tool_use results
• Use tool_choice: "any" to guarantee structured output when multiple schemas exist
• Force a specific tool call: tool_choice: {"type": "tool", "name": "extract_metadata"}
• Make fields optional/nullable when the source may not contain information to avoid fabricating values
• Use enum values like "unclear" and "other" plus detail fields for extensible categorization

4.4 Implementing Validation, Retries, and Feedback Loops for Extraction Quality

Key knowledge:

• Retry-with-error-feedback: include concrete validation errors in the retry prompt to guide corrections
• Retries are ineffective when the information is simply absent from the source
• Feedback loop design: track the pattern that triggered a finding (detected_pattern)
• Semantic errors (totals don’t reconcile) vs syntax errors (addressed by tool_use)

Key skills:

• Follow-up prompts with the original document, an incorrect extraction, and specific validation errors
• Identify when retry will be ineffective (the required info is only in an external document)
• Include detected_pattern fields in findings to analyze false positives
• Design self-correction by extracting both calculated_total and stated_total to detect discrepancies

4.5 Designing Efficient Batch Processing Strategies

Key knowledge:

• Message Batches API: 50% savings, up to 24-hour processing window, no latency SLA guarantees
• Batch processing is suitable for non-blocking tasks (overnight reports, audits) and not suitable for blocking tasks (pre-merge checks)
• Batch API does not support multi-turn tool calling within a single request
• custom_id fields correlate request/response within batches

Key skills:

• Use synchronous API for blocking checks; use Batch API for overnight/weekly workloads
• Plan batch submission cadence based on SLA needs (e.g., 4-hour windows for a 30-hour guarantee with 24-hour processing)
• Handle failures by re-submitting only failed documents (identified by custom_id)
• Iterate on prompts using a sample before running large-scale processing

4.6 Designing Multi-instance and Multi-pass Review Architectures

Key knowledge:

• Self-review limitations: the model retains its reasoning context and is less likely to challenge its own decisions
• Independent review instances (without generation context) are better at finding subtle issues
• Multi-pass review: per-file local analysis plus a cross-file integration pass to avoid attention dilution

Key skills:

• Use a second independent Claude instance to review changes without generation context
• Split multi-file reviews into per-file passes plus integration passes for cross-file dataflow analysis
• Use verification passes with self-rated confidence to route reviews in a calibrated way

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