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updated 2026-05-28

Domain 4 — Prompt Engineering & Structured Output (20%)

How to write prompts that work and how to get reliable structured data out.

Topics

4.1 The five (+ two) prompt levers

  1. Be clear and direct.
  2. Few-shot examples (2–5).
  3. Let Claude think (CoT or extended thinking).
  4. XML tags for structure.
  5. Assign a role.
  6. Prefill assistant turn.
  7. Chain prompts.

Apply in order; only escalate when needed.

4.2 System prompt structure

  • Identity / role
  • Mission
  • Tone / style
  • Capabilities & tools
  • Boundaries / NEVERs
  • Output format
  • Few-shot examples

Critical rules at top AND end of system prompt (recency + primacy).

4.3 Principles vs conditionals

  • Principles ("Always cite sources") survive dilution.
  • Conditionals ("If user mentions X, do Y") accumulate and break.
  • Prefer principles; minimize conditionals; split into subagents when conditionals proliferate.

4.4 Dilution

  • Long prompts with many same-priority rules cause Claude to weight poorly.
  • Mitigations: move criticals to ends; cache stable doc material; split via subagents.

4.5 Few-shot

  • 3–5 examples in XML tags.
  • Cover edges: typical, ambiguous, refusal.
  • Format examples identically to desired output (down to whitespace).
  • Diversity > similarity.

4.6 Structured output — three techniques (ranked)

  1. Force tool use with tool_choice = {type: "tool", name} — most reliable.
  2. Prefill assistant turn with { or <output>.
  3. Prompt-only ("respond in JSON matching...") — least reliable.

4.7 JSON schemas

  • Use enum for fixed sets.
  • Mark nullable explicitly.
  • Minimize required to truly required.
  • Add description to every property.
  • Flatten when possible; deep nesting → mistakes.

4.8 Validation loops

  • Always validate after generation (jsonschema, pydantic).
  • On failure: re-prompt with validator error included in user turn.
  • Cap retries (~3) → route to human.

4.9 Provenance for extraction

  • Pair every extracted value with a source pointer.
  • Schemas can require it (vendor_source etc.).
  • Enables audit + spot-check.

4.10 Batch processing

  • Use messages.batches API for bulk extraction.
  • 50% input + output discount.
  • Async, up to 24h SLA.
  • Per-request custom_id to map back.
  • Mix models in one batch for eval.

4.11 Evaluations

  • Always have an eval set before tweaking prompts.
  • Compare two prompt versions side-by-side on the same eval.
  • Track regressions; a "better" change for one input class may hurt another.

Common question shapes

  • "JSON output is inconsistent. What's the most reliable fix?" → Force tool use.
  • "Prompt is long; one rule keeps being ignored. Why?" → Dilution; move rule to ends or restructure.
  • "Bulk classify 200K rows. Best approach?" → Message Batches + Haiku + few-shot in cached system prompt.
  • "Extraction must be auditable." → Include provenance pointers in schema.
  • "Validation fails 10% of the time." → Validation loop with retry + cap + human escalation.

Cheatsheet

  • Force structure via tools, not prose.
  • Few-shot for tone/format.
  • Principles > conditionals.
  • Validate after generate, retry with error.
  • Batch for bulk offline.

See also