ai-prompt-engineering

name	ai-prompt-engineering
description	Operational prompt engineering patterns, templates, and validation flows for Claude Code.

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.

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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.

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Quick Reference

Task	Pattern to Use	Key Components	When to Use
Machine-parseable output	Structured Output	JSON schema, "JSON-only" directive, no prose	API integrations, data extraction
Field extraction	Deterministic Extractor	Exact schema, missing→null, no transformations	Form data, invoice parsing
Use retrieved context	RAG Workflow	Context relevance check, chunk citations, explicit missing info	Knowledge bases, documentation search
Internal reasoning	Hidden Chain-of-Thought	Internal reasoning, final answer only	Classification, complex decisions
Tool-using agent	Tool/Agent Planner	Plan-then-act, one tool per turn	Multi-step workflows, API calls
Text transformation	Rewrite + Constrain	Style rules, meaning preservation, format spec	Content adaptation, summarization
Classification	Decision Tree	Ordered branches, mutually exclusive, JSON result	Routing, categorization, triage

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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**

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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

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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

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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

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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

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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