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 prompt_optimizer import PromptTemplate, FewShotSelector
# Define a structured prompt template
template = PromptTemplate(
system="You are an expert SQL developer. Generate efficient, secure SQL queries.",
instruction="Convert the following natural language query to SQL:\n{query}",
few_shot_examples=True,
output_format="SQL code block with explanatory comments"
)
# Configure few-shot learning
selector = FewShotSelector(
examples_db="sql_examples.jsonl",
selection_strategy="semantic_similarity",
max_examples=3
)
# Generate optimized prompt
prompt = template.render(
query="Find all users who registered in the last 30 days",
examples=selector.select(query="user registration date filter")
)
Key Patterns
Progressive Disclosure
Start with simple prompts, add complexity only when needed:
Level 1: Direct instruction
"Summarize this article"
Level 2: Add constraints
"Summarize this article in 3 bullet points, focusing on key findings"
Level 3: Add reasoning
"Read this article, identify the main findings, then summarize in 3 bullet points"
Level 4: Add examples
Include 2-3 example summaries with input-output pairs
Ask for alternative interpretations when uncertain
Specify how to indicate missing information
Best Practices
Be Specific: Vague prompts produce inconsistent results
Show, Don't Tell: Examples are more effective than descriptions
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
Integration Patterns
With RAG Systems
# Combine retrieved context with prompt engineering
prompt = f"""Given the following context:
{retrieved_context}
{few_shot_examples}
Question: {user_question}
Provide a detailed answer based solely on the context above. If the context doesn't contain enough information, explicitly state what's missing."""
With Validation
# Add self-verification step
prompt = f"""{main_task_prompt}
After generating your response, verify it meets these criteria:
1. Answers the question directly
2. Uses only information from provided context
3. Cites specific sources
4. Acknowledges any uncertainty
If verification fails, revise your response."""
Performance Optimization
Token Efficiency
Remove redundant words and phrases
Use abbreviations consistently after first definition
Consolidate similar instructions
Move stable content to system prompts
Latency Reduction
Minimize prompt length without sacrificing quality
Use streaming for long-form outputs
Cache common prompt prefixes
Batch similar requests when possible
Resources
references/few-shot-learning.md: Deep dive on example selection and construction
