prompt-engineering-patterns

name prompt-engineering-patterns description This skill should be used when the user asks to "optimize a prompt", "improve prompt performance", "design a prompt template", "write better prompts", "debug prompt issues", "use chain-of-thought", "structured prompting", "few-shot prompting", or wants to apply advanced prompt engineering patterns for production LLM applications. Prompt Engineering Patterns Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability. When to Use This Skill Designing complex prompts for production LLM applications Optimizing prompt performance and consistency Implementing structured reasoning patterns (chain-of-thought, tree-of-thought) Building few-shot learning systems with dynamic example selection Creating reusable prompt templates with variable interpolation Debugging and refining prompts that produce inconsistent outputs Implementing system prompts for specialized AI assistants Using structured outputs (JSON mode) for reliable parsing Core Capabilities 1. Few-Shot Learning Example selection strategies (semantic similarity, diversity sampling) Balancing example count with context window constraints Constructing effective demonstrations with input-output pairs Dynamic example retrieval from knowledge bases Handling edge cases through strategic example selection 2. Chain-of-Thought Prompting Step-by-step reasoning elicitation Zero-shot CoT with "Let's think step by step" Few-shot CoT with reasoning traces Self-consistency techniques (sampling multiple reasoning paths) Verification and validation steps 3. Structured Outputs JSON mode for reliable parsing Pydantic schema enforcement Type-safe response handling Error handling for malformed outputs 4. Prompt Optimization Iterative refinement workflows A/B testing prompt variations Measuring prompt performance metrics (accuracy, consistency, latency) Reducing token usage while maintaining quality Handling edge cases and failure modes 5. Template Systems Variable interpolation and formatting Conditional prompt sections Multi-turn conversation templates Role-based prompt composition Modular prompt components 6. System Prompt Design Setting model behavior and constraints Defining output formats and structure Establishing role and expertise Safety guidelines and content policies Context setting and background information Quick Start from langchain_anthropic import ChatAnthropic from langchain_core.prompts import ChatPromptTemplate from pydantic import BaseModel, Field # Define structured output schema class SQLQuery ( BaseModel ): query: str = Field(description= "The SQL query" ) explanation: str = Field(description= "Brief explanation of what the query does" ) tables_used: list [ str ] = Field(description= "List of tables referenced" ) # Initialize model with structured output llm = ChatAnthropic(model= "claude-sonnet-5" ) structured_llm = llm.with_structured_output(SQLQuery) # Create prompt template prompt = ChatPromptTemplate.from_messages([ ( "system" , """You are an expert SQL developer. Generate efficient, secure SQL queries. Always use parameterized queries to prevent SQL injection. Explain your reasoning briefly.""" ), ( "user" , "Convert this to SQL: {query}" ) ]) # Create chain chain = prompt | structured_llm # Use result = await chain.ainvoke({ "query" : "Find all users who registered in the last 30 days" }) print (result.query) print (result.explanation) Detailed patterns and worked examples Detailed pattern documentation lives in references/details.md . Read that file when the navigation tier above is insufficient. Best Practices Be Specific : Vague prompts produce inconsistent results Show, Don't Tell : Examples are more effective than descriptions Use Structured Outputs : Enforce schemas with Pydantic for reliability Test Extensively : Evaluate on diverse, representative inputs Iterate Rapidly : Small changes can have large impacts Monitor Performance : Track metrics in production Version Control : Treat prompts as code with proper versioning Document Intent : Explain why prompts are structured as they are Common Pitfalls Over-engineering : Starting with complex prompts before trying simple ones Example pollution : Using examples that don't match the target task Context overflow : Exceeding token limits with excessive examples Ambiguous instructions : Leaving room for multiple interpretations Ignoring edge cases : Not testing on unusual or boundary inputs No error handling : Assuming outputs will always be well-formed Hardcoded values : Not parameterizing prompts for reuse Success Metrics Track these KPIs for your prompts: Accuracy : Correctness of outputs Consistency : Reproducibility across similar inputs Latency : Response time (P50, P95, P99) Token Usage : Average tokens per request Success Rate : Percentage of valid, parseable outputs User Satisfaction : Ratings and feedback