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

1. Be Specific: Vague prompts produce inconsistent results
2. Show, Don't Tell: Examples are more effective than descriptions
3. Use Structured Outputs: Enforce schemas with Pydantic for reliability
4. Test Extensively: Evaluate on diverse, representative inputs
5. Iterate Rapidly: Small changes can have large impacts
6. Monitor Performance: Track metrics in production
7. Version Control: Treat prompts as code with proper versioning
8. 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

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