AI Prompt Engineering

Prompt Engineering — Operational Skill

Modern Best Practices (December 2025): versioned prompts, explicit output contracts, regression tests, and safety threat modeling for tool/RAG prompts (OWASP LLM Top 10: https://owasp.org/www-project-top-10-for-large-language-model-applications/).

This skill provides operational guidance for building production-ready prompts across standard tasks, RAG workflows, agent orchestration, structured outputs, hidden reasoning, and multi-step planning.

All content is operational, not theoretical. Focus on patterns, checklists, and copy-paste templates.

Claude 4+ Updates: This skill includes Claude 4.x and 4.5-specific optimizations:

• Action directives: Frame for implementation, not suggestions
• Parallel tool execution: Independent tool calls can run simultaneously
• Long-horizon task management: State tracking, incremental progress, context compaction resilience
• Positive framing: Describe desired behavior rather than prohibitions
• Style matching: Prompt formatting influences output style
• Domain-specific patterns: Specialized guidance for frontend, research, and agentic coding
• Style-adversarial resilience: Stress-test refusals with poetic/role-play rewrites; normalize or decline stylized harmful asks before tool use

Claude 4.5 Communication: Claude 4.5 is more concise by default. Request explicit summaries when needed for visibility into reasoning or work completed.

When to Use This Skill

Activate this skill when the user asks to:

• Write or improve a production-ready prompt
• Debug prompt failures or inconsistent outputs
• Create structured outputs (JSON, tables, schemas)
• Build deterministic extractors
• Design RAG pipelines with context grounding
• Implement agent workflows with tool calling
• Add hidden reasoning (CoT) without visible output
• Convert user tasks into reusable templates
• Validate prompt quality against operational checklists
• Standardize output formats across systems

Do NOT use this skill for:

• LLM theory or model architecture explanations
• General educational content about AI
• Historical background on prompt engineering

See Also: For specialized AI/LLM implementations, see "Related Skills" section at the end of this document.

Quick Reference

Decision Tree: Choosing the Right Pattern

User needs: [Prompt Type]
    ├─ Output must be machine-readable?
    │   ├─ Extract specific fields only? → **Deterministic Extractor Pattern**
    │   └─ Generate structured data? → **Structured Output Pattern (JSON)**
    │
    ├─ Use external knowledge?
    │   └─ Retrieved context must be cited? → **RAG Workflow Pattern**
    │
    ├─ Requires reasoning but hide process?
    │   └─ Classification or decision task? → **Hidden Chain-of-Thought Pattern**
    │
    ├─ Needs to call external tools/APIs?
    │   └─ Multi-step workflow? → **Tool/Agent Planner Pattern**
    │
    ├─ Transform existing text?
    │   └─ Style/format constraints? → **Rewrite + Constrain Pattern**
    │
    └─ Classify or route to categories?
        └─ Mutually exclusive rules? → **Decision Tree Pattern**

Core Concepts vs Implementation Practices

Core Concepts (Vendor-Agnostic)

• Prompt contract: inputs, allowed tools, output schema, max tokens, and refusal rules.
• Determinism controls: temperature/top_p, constrained decoding/structured outputs, and strict formatting.
• Cost & latency budgets: prompt length and max output drive tokens and tail latency; enforce hard limits and measure p95/p99.
• Evaluation: golden sets + regression gates + A/B + post-deploy monitoring.
• Security: prompt injection, data exfiltration, and tool misuse are primary threats (OWASP LLM Top 10: https://owasp.org/www-project-top-10-for-large-language-model-applications/).

Implementation Practices (Model/Platform-Specific)

• Use model-specific structured output features when available; keep a schema validator as the source of truth.
• Align tracing/metrics with OpenTelemetry GenAI semantic conventions (https://opentelemetry.io/docs/specs/semconv/gen-ai/).

Do / Avoid

Do

• Do keep prompts small and modular; centralize shared fragments (policies, schemas, style).
• Do add a prompt eval harness and block merges on regressions.
• Do prefer “brief justification” over requesting chain-of-thought; treat hidden reasoning as model-internal.

Avoid

• Avoid prompt sprawl (many near-duplicates with no owner or tests).
• Avoid brittle multi-step chains without intermediate validation.
• Avoid mixing policy and product copy in the same prompt (harder to audit and update).

Navigation: Core Patterns

• Core Patterns - 7 production-grade prompt patterns
  • Structured Output (JSON), Deterministic Extractor, RAG Workflow
  • Hidden Chain-of-Thought, Tool/Agent Planner, Rewrite + Constrain, Decision Tree
  • Each pattern includes structure template and validation checklist

Navigation: Best Practices

• Best Practices (Core) - Foundation rules for production-grade prompts

  • System instruction design, output contract specification, action directives
  • Context handling, error recovery, positive framing, style matching, style-adversarial red teaming
  • Anti-patterns, Claude 4+ specific optimizations

• Production Guidelines - Deployment and operational guidance

  • Evaluation & testing (Prompt CI/CD), model parameters, few-shot selection
  • Safety & guardrails, conversation memory, context compaction resilience
  • Answer engineering, decomposition, multilingual/multimodal, benchmarking

• Quality Checklists - Validation checklists before deployment

  • Prompt QA, JSON validation, agent workflow checks
  • RAG workflow, safety & security, performance optimization
  • Testing coverage, anti-patterns, quality score rubric

• Domain-Specific Patterns - Claude 4+ optimized patterns for specialized domains

  • Frontend/visual code: Creativity encouragement, design variations, micro-interactions
  • Research tasks: Success criteria, verification, hypothesis tracking
  • Agentic coding: No speculation rule, principled implementation, investigation patterns
  • Cross-domain best practices and quality modifiers

Navigation: Specialized Patterns

• RAG Patterns - Retrieval-augmented generation workflows

  • Context grounding, chunk citation, missing information handling

• Agent and Tool Patterns - Tool use and agent orchestration

  • Plan-then-act workflows, tool calling, multi-step reasoning, generate–verify–revise chains with role-play + few-shot + targeted CoT per sub-agent

• Extraction Patterns - Deterministic field extraction

  • Schema-based extraction, null handling, no hallucinations

• Reasoning Patterns (Hidden CoT) - Internal reasoning without visible output

  • Hidden reasoning, final answer only, classification workflows

• Additional Patterns - Extended prompt engineering techniques

  • Advanced patterns, edge cases, optimization strategies

Navigation: Templates

Templates are copy-paste ready and organized by complexity:

Quick Templates

• Quick Template - Fast, minimal prompt structure

Standard Templates

• Standard Template - Production-grade operational prompt
• Agent Template - Tool-using agent with planning
• RAG Template - Retrieval-augmented generation
• Chain-of-Thought Template - Hidden reasoning pattern
• JSON Extractor Template - Deterministic field extraction
• Prompt Evaluation Template - Regression tests, A/B testing, rollout gates

External Resources

External references are listed in data/sources.json:

• Official documentation (OpenAI, Anthropic, Google)
• LLM frameworks (LangChain, LlamaIndex)
• Vector databases (Pinecone, Weaviate, FAISS)
• Evaluation tools (OpenAI Evals, HELM)
• Safety guides and standards
• RAG and retrieval resources

Related Skills

This skill provides foundational prompt engineering patterns. For specialized implementations:

AI/LLM Skills:

• AI Agents Development - Production agent patterns, MCP integration, orchestration
• AI LLM Engineering - LLM application architecture and deployment
• AI LLM RAG Engineering - Advanced RAG pipelines and chunking strategies
• AI LLM Search & Retrieval - Search optimization, hybrid retrieval, reranking
• AI LLM Development - Fine-tuning, evaluation, dataset creation

Software Development Skills:

• Software Architecture Design - System design patterns
• Software Backend - Backend implementation
• Foundation API Design - API design and contracts

Usage Notes

For Claude Code:

• Reference this skill when building prompts for agents, commands, or integrations
• Use Quick Reference table for fast pattern lookup
• Follow Decision Tree to select appropriate pattern
• Validate outputs with Quality Checklists before deployment
• Use templates as starting points, customize for specific use cases