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.

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