Prompt Engineering

Comprehensive prompt engineering knowledge base. Provides actionable patterns, checklists, and guides for designing, securing, evaluating, and optimizing LLM prompts and agent systems.

When to Use

• Designing or reviewing prompt templates (system, developer, user prompts)
• Building tool calling schemas and structured output contracts
• Evaluating prompt quality — accuracy, safety, cost, latency
• Auditing LLM security against OWASP LLM Top 10
• Designing multi-agent orchestration and handoff protocols
• Optimizing prompt cost and latency
• Creating or reviewing Claude Code AI assets (rules, workflows, skills — all are prompts)
• Setting up prompt versioning and observability

When NOT to Use

• Implementing backend/frontend code (use Agent(software-engineer) + stack-specific role)
• Infrastructure and deployment (use Agent(devops-engineer))
• Writing code tests (use Agent(qa-engineer) + testing-procedures skill)
• Content writing (use Agent(content-writer))
• Context pipeline design, memory engineering, agent harness, RAG architecture, multi-agent orchestration, production AI checklists → use context-engineering skill

Key Concepts

Prompt as System

A prompt is not a string — it is a system composed of:

1. Instruction hierarchy: System prompt > Developer prompt > User prompt > Retrieved content
2. Context assembly: What enters the context window, in what order, with what priority
3. Output contract: Schema, format, constraints, error handling, fallback behavior
4. Tool interface: Available tools, their schemas, permissions, composition patterns
5. Guard rails: Safety filters, refusal policies, output validators
6. Versioning: Immutable versions, deployment tags, audit trail

Core Principles

1. Eval-first: Define how to measure before changing anything
2. Simplest technique: Zero-shot → few-shot → CoT → chaining. Escalate only when simpler fails
3. Explicit over implicit: Spell out constraints, output format, edge cases. Never assume the model "knows"
4. Separation of concerns: Instructions vs data vs examples — always delimited
5. Grounding: Prefer citations and verifiable data over unanchored claims
6. Least privilege: Minimal tool permissions per agent. HITL for high-impact actions
7. Cost awareness: Every token costs money and time. Compress, cache, route

Resource Files

Integration

• Follows rules: Agent(prompt-engineer) (prompt system architecture, security, eval-first quality)
• Used by workflows: /ai-assets (all assets are prompts), /feature-dev (AI features), /code-review (prompt quality review)
• Companion skills: context-engineering skill (context pipeline design, memory engineering, agent harness, RAG architecture, multi-agent orchestration, production checklists), asset-validation skill (AI asset format validation), code-review skill (review checklists)
• Collaborates with roles: Agent(software-engineer) (prompt integration), Agent(qa-engineer) (prompt regression tests), Agent(product-manager) (success metrics)