Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.
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
