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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

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Decision Tree: Choosing the Right Pattern

CopyUser 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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Context Engineering (2026)

True expertise in prompting extends beyond writing instructions to shaping the entire context in which the model operates. Context engineering encompasses:

• Conversation history: What prior turns inform the current response
• Retrieved context (RAG): External knowledge injected into the prompt
• Structured inputs: JSON schemas, system/user message separation
• Tool outputs: Results from previous tool calls that shape next steps

Context Engineering vs Prompt Engineering

Key Context Engineering Patterns

1. Context Prioritization: Place most relevant information first; models attend more strongly to early context.

2. Context Compression: Summarize history, truncate tool outputs, select most relevant RAG chunks.

3. Context Separation: Use clear delimiters (<system>, <user>, <context>) to separate instruction types.

4. Dynamic Context: Adjust context based on task complexity—simple tasks need less context, complex tasks need more.

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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.
• Context engineering: conversation history, RAG context, tool outputs, and structured inputs shape model behavior.
• 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
  • CI/CD Tools (2026): Promptfoo, DeepEval integration patterns
  • Security (2026): PromptGuard 4-layer defense, Microsoft Prompt Shields, taint tracking

• 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
  • Multi-Agent Orchestration (2026): centralized, handoff, federated patterns; plan-and-execute (90% cost reduction)

• 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
  • Extended Thinking API (Claude 4+): budget management, think tool, multishot patterns

• 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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Trend Awareness Protocol

IMPORTANT: When users ask recommendation questions about prompt engineering, you MUST use WebSearch to check current trends before answering.

Trigger Conditions

• "What's the best prompting technique for [use case]?"
• "What should I use for [structured output/reasoning/agents]?"
• "What's the latest in prompt engineering?"
• "Current best practices for [chain-of-thought/few-shot/system prompts]?"
• "Is [prompting technique] still effective in 2026?"
• "How do I prompt [Claude 4/GPT-4.5/Gemini 2]?"
• "Best way to get reliable [JSON/structured output]?"

Required Searches

1. Search: "prompt engineering best practices 2026"
2. Search: "[Claude/GPT/Gemini] prompting techniques 2026"
3. Search: "prompt engineering trends January 2026"
4. Search: "[reasoning/structured output/agents] prompting 2026"

What to Report

After searching, provide:

• Current landscape: What prompting techniques work best NOW (model-specific)
• Emerging trends: New techniques gaining traction (thinking tokens, etc.)
• Deprecated/declining: Techniques that no longer work well on new models
• Recommendation: Based on fresh data and model-specific documentation

Example Topics (verify with fresh search)

• Model-specific prompting (Claude 4.x, GPT-4.5, Gemini 2.x)
• Reasoning techniques (extended thinking, chain-of-thought variants)
• Structured output (JSON mode, function calling, tool use)
• Agent prompting patterns (ReAct, plan-and-execute)
• Safety and guardrails in prompts
• Evaluation and testing of prompts

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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