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:

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

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

• 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

• 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

• Review the prompt template library for common patterns

• Experiment with few-shot learning for your specific use case

• Implement prompt versioning and A/B testing

• Set up automated evaluation pipelines

• Document your prompt engineering decisions and learnings