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Prompt Engineering

A systematic skill for designing, testing, and optimizing prompts for large language models. This skill covers advanced prompting techniques, template management, and best practices for reliable LLM applications.

Status: 🔵 Master Plan Available

Key Topics​

  • Core Prompting Techniques

    • Zero-shot and few-shot learning
    • Chain-of-thought reasoning
    • Role-based prompting
    • System/user/assistant message design

  • Advanced Patterns

    • ReAct (Reasoning + Acting)
    • Tree of Thoughts
    • Self-consistency decoding
    • Constitutional AI principles
    • Retrieval-augmented generation (RAG)

  • Prompt Optimization

    • Iterative refinement workflows
    • A/B testing prompts
    • Parameter tuning (temperature, top-p, etc.)
    • Token optimization strategies
    • Context window management

  • Production Considerations

    • Prompt versioning and tracking
    • Template management systems
    • Safety and guardrails
    • Cost optimization
    • Latency reduction

Primary Tools & Technologies​

  • LLM Providers: OpenAI, Anthropic Claude, Google Gemini, Cohere
  • Prompt Management: LangChain, LlamaIndex, Semantic Kernel, Haystack
  • Testing & Evaluation: PromptFoo, Pezzo, OpenAI Evals
  • Observability: LangSmith, Helicone, Traceloop, Weights & Biases
  • RAG Frameworks: LangChain, LlamaIndex, Haystack, txtai
  • Vector Databases: Pinecone, Weaviate, Qdrant, Chroma

Integration Points​

  • LLM Evaluation: Testing prompt effectiveness, quality metrics
  • Embedding Optimization: RAG implementation, semantic search
  • API Design: Prompt endpoint design, versioning
  • Content Moderation: Safety filters, output validation
  • UI/UX: Chat interfaces, streaming responses

Prompting Strategies by Use Case​

Information Extraction​

Extract structured data from text:
- Use clear output format specifications
- Provide examples (few-shot)
- Specify required fields explicitly
- Handle edge cases in instructions

Content Generation​

Generate high-quality content:
- Define tone, style, and audience
- Provide context and constraints
- Use role-based prompting
- Iterate with feedback loops

Reasoning & Analysis​

Complex problem-solving:
- Use chain-of-thought prompting
- Break problems into steps
- Request explanations
- Verify logic with self-consistency

Classification & Routing​

Categorize inputs efficiently:
- Provide clear category definitions
- Use examples for each class
- Optimize for token efficiency
- Implement confidence thresholds

Prompt Template Structure​

Basic Template​

System: [Role and behavior instructions]
Context: [Relevant background information]
Task: [Specific request with constraints]
Format: [Expected output structure]
Examples: [Few-shot demonstrations]

RAG-Enhanced Template​

System: [Role definition]
Context: [Retrieved relevant documents]
Query: [User question]
Instructions: [How to use context]
Constraints: [Citation requirements, limitations]

Best Practices​

  • Clarity: Be explicit and specific in instructions
  • Structure: Use markdown formatting for readability
  • Examples: Provide diverse, representative few-shot examples
  • Constraints: Specify length, format, and tone requirements
  • Iteration: Test and refine based on actual outputs
  • Version Control: Track prompt changes and performance
  • Safety: Implement content filtering and validation
  • Cost Awareness: Optimize token usage without sacrificing quality

Common Pitfalls​

  • Vague or ambiguous instructions
  • Over-reliance on model "understanding" context
  • Inconsistent few-shot examples
  • Ignoring token limits and costs
  • Lack of output validation
  • Not testing across model versions
  • Insufficient error handling
  • Poor prompt versioning practices

Success Metrics​

  • Quality: Output accuracy, relevance, coherence
  • Reliability: Consistency across similar inputs
  • Efficiency: Token usage, latency, cost per request
  • Safety: Harmful content rate, guardrail effectiveness
  • User Satisfaction: Human evaluation scores, feedback ratings