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

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. 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

4. Template Systems

• •Variable interpolation and formatting
• •Conditional prompt sections
• •Multi-turn conversation templates
• •Role-based prompt composition
• •Modular prompt components

5. 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 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:

1. •

   Level 1: Direct instruction

   • •"Summarize this article"
2. •

   Level 2: Add constraints

   • •"Summarize this article in 3 bullet points, focusing on key findings"
3. •

   Level 3: Add reasoning

   • •"Read this article, identify the main findings, then summarize in 3 bullet points"
4. •

   Level 4: Add examples

   • •Include 2-3 example summaries with input-output pairs

Instruction Hierarchy

[System Context] → [Task Instruction] → [Examples] → [Input Data] → [Output Format]

Error Recovery

Build prompts that gracefully handle failures:

• •Include fallback instructions
• •Request confidence scores
• •Ask for alternative interpretations when uncertain
• •Specify how to indicate missing information

Best Practices

1. •Be Specific: Vague prompts produce inconsistent results
2. •Show, Don't Tell: Examples are more effective than descriptions
3. •Test Extensively: Evaluate on diverse, representative inputs
4. •Iterate Rapidly: Small changes can have large impacts
5. •Monitor Performance: Track metrics in production
6. •Version Control: Treat prompts as code with proper versioning
7. •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
• •references/chain-of-thought.md: Advanced reasoning elicitation techniques
• •references/prompt-optimization.md: Systematic refinement workflows
• •references/prompt-templates.md: Reusable template patterns
• •references/system-prompts.md: System-level prompt design
• •assets/prompt-template-library.md: Battle-tested prompt templates
• •assets/few-shot-examples.json: Curated example datasets
• •scripts/optimize-prompt.py: Automated prompt optimization tool

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 outputs
• •User Satisfaction: Ratings and feedback

Next Steps

1. •Review the prompt template library for common patterns
2. •Experiment with few-shot learning for your specific use case
3. •Implement prompt versioning and A/B testing
4. •Set up automated evaluation pipelines
5. •Document your prompt engineering decisions and learnings