prompt-engineering

name	prompt-engineering
category	backend
tags	["prompt-engineering","few-shot-learning","chain-of-thought","optimization","templates","system-prompts","llm-performance","ai-patterns"]
version	1.0.0
description	This skill should be used when creating, optimizing, or implementing advanced prompt patterns including few-shot learning, chain-of-thought reasoning, prompt optimization workflows, template systems, and system prompt design. It provides comprehensive frameworks for building production-ready prompts with measurable performance improvements.
allowed-tools	Read, Write, Edit, Glob, Grep, Bash

Prompt Engineering

Overview

This skill provides comprehensive frameworks for creating, optimizing, and implementing advanced prompt patterns that significantly improve LLM performance across various tasks and models. It covers few-shot learning, chain-of-thought reasoning, prompt optimization workflows, template systems, and system prompt design for production-ready AI applications.

When to Use

Use this skill when:

• Creating new prompts for complex reasoning or analytical tasks
• Optimizing existing prompts for better accuracy or efficiency
• Implementing few-shot learning with strategic example selection
• Designing chain-of-thought reasoning for multi-step problems
• Building reusable prompt templates and systems
• Developing system prompts for consistent model behavior
• Troubleshooting poor prompt performance or failure modes
• Scaling prompt systems for production use cases

Core Prompt Engineering Patterns

1. Few-Shot Learning Implementation

Select examples using semantic similarity and diversity sampling to maximize learning within context window constraints.

Example Selection Strategy

• Use references/few-shot-patterns.md for comprehensive selection frameworks
• Balance example count (3-5 optimal) with context window limitations
• Include edge cases and boundary conditions in example sets
• Prioritize diverse examples that cover problem space variations
• Order examples from simple to complex for progressive learning

Few-Shot Template Structure

Example 1 (Basic case):
Input: {representative_input}
Output: {expected_output}

Example 2 (Edge case):
Input: {challenging_input}
Output: {robust_output}

Example 3 (Error case):
Input: {problematic_input}
Output: {corrected_output}

Now handle: {target_input}

2. Chain-of-Thought Reasoning

Elicit step-by-step reasoning for complex problem-solving through structured thinking patterns.

Implementation Patterns

• Reference references/cot-patterns.md for detailed reasoning frameworks
• Use "Let's think step by step" for zero-shot CoT initiation
• Provide complete reasoning traces for few-shot CoT demonstrations
• Implement self-consistency by sampling multiple reasoning paths
• Include verification and validation steps in reasoning chains

CoT Template Structure

Let's approach this step-by-step:

Step 1: {break_down_the_problem}
Analysis: {detailed_reasoning}

Step 2: {identify_key_components}
Analysis: {component_analysis}

Step 3: {synthesize_solution}
Analysis: {solution_justification}

Final Answer: {conclusion_with_confidence}

3. Prompt Optimization Workflows

Implement iterative refinement processes with measurable performance metrics and systematic A/B testing.

Optimization Process

• Use references/optimization-frameworks.md for comprehensive optimization strategies
• Measure baseline performance before optimization attempts
• Implement single-variable changes for accurate attribution
• Track metrics: accuracy, consistency, latency, token efficiency
• Use statistical significance testing for A/B validation
• Document optimization iterations and their impacts

Performance Metrics Framework

• Accuracy: Task completion rate and output correctness
• Consistency: Response stability across multiple runs
• Efficiency: Token usage and response time optimization
• Robustness: Performance across edge cases and variations
• Safety: Adherence to guidelines and harm prevention

4. Template Systems Architecture

Build modular, reusable prompt components with variable interpolation and conditional sections.

Template Design Principles

• Reference references/template-systems.md for modular template frameworks
• Use clear variable naming conventions (e.g., {user_input}, {context})
• Implement conditional sections for different scenario handling
• Design role-based templates for specific use cases
• Create hierarchical template composition patterns

Template Structure Example

# System Context
You are a {role} with {expertise_level} expertise in {domain}.

# Task Context
{if background_information}
Background: {background_information}
{endif}

# Instructions
{task_instructions}

# Examples
{example_count}

# Output Format
{output_specification}

# Input
{user_query}

5. System Prompt Design

Design comprehensive system prompts that establish consistent model behavior, output formats, and safety constraints.

System Prompt Components

• Use references/system-prompt-design.md for detailed design guidelines
• Define clear role specification and expertise boundaries
• Establish output format requirements and structural constraints
• Include safety guidelines and content policy adherence
• Set context for background information and domain knowledge

System Prompt Framework

You are an expert {role} specializing in {domain} with {experience_level} of experience.

## Core Capabilities
- List specific capabilities and expertise areas
- Define scope of knowledge and limitations

## Behavioral Guidelines
- Specify interaction style and communication approach
- Define error handling and uncertainty protocols
- Establish quality standards and verification requirements

## Output Requirements
- Specify format expectations and structural requirements
- Define content inclusion and exclusion criteria
- Establish consistency and validation requirements

## Safety and Ethics
- Include content policy adherence
- Specify bias mitigation requirements
- Define harm prevention protocols

Implementation Workflows

Workflow 1: Create New Prompt from Requirements

1. Analyze Requirements

   • Identify task complexity and reasoning requirements
   • Determine target model capabilities and limitations
   • Define success criteria and evaluation metrics
   • Assess need for few-shot learning or CoT reasoning

2. Select Pattern Strategy

   • Use few-shot learning for classification or transformation tasks
   • Apply CoT for complex reasoning or multi-step problems
   • Implement template systems for reusable prompt architecture
   • Design system prompts for consistent behavior requirements

3. Draft Initial Prompt

   • Structure prompt with clear sections and logical flow
   • Include relevant examples or reasoning demonstrations
   • Specify output format and quality requirements
   • Incorporate safety guidelines and constraints

4. Validate and Test

   • Test with diverse input scenarios including edge cases
   • Measure performance against defined success criteria
   • Iterate refinement based on testing results
   • Document optimization decisions and their rationale

Workflow 2: Optimize Existing Prompt

1. Performance Analysis

   • Measure current prompt performance metrics
   • Identify failure modes and error patterns
   • Analyze token efficiency and response latency
   • Assess consistency across multiple runs

2. Optimization Strategy

   • Apply systematic A/B testing with single-variable changes
   • Use few-shot learning to improve task adherence
   • Implement CoT reasoning for complex task components
   • Refine template structure for better clarity

3. Implementation and Testing

   • Deploy optimized prompts with controlled rollout
   • Monitor performance metrics in production environment
   • Compare against baseline using statistical significance
   • Document improvements and lessons learned

Workflow 3: Scale Prompt Systems

1. Modular Architecture Design

   • Decompose complex prompts into reusable components
   • Create template inheritance hierarchies
   • Implement dynamic example selection systems
   • Build automated quality assurance frameworks

2. Production Integration

   • Implement prompt versioning and rollback capabilities
   • Create performance monitoring and alerting systems
   • Build automated testing frameworks for prompt validation
   • Establish update and deployment workflows

Quality Assurance

Validation Requirements

• Test prompts with at least 10 diverse scenarios
• Include edge cases, boundary conditions, and failure modes
• Verify output format compliance and structural consistency
• Validate safety guideline adherence and harm prevention
• Measure performance across multiple model runs

Performance Standards

• Achieve >90% task completion for well-defined use cases
• Maintain <5% variance across multiple runs for consistency
• Optimize token usage without sacrificing accuracy
• Ensure response latency meets application requirements
• Demonstrate robust handling of edge cases and unexpected inputs

Integration with Other Skills

This skill integrates seamlessly with:

• langchain4j-ai-services-patterns: Interface-based prompt design
• langchain4j-rag-implementation-patterns: Context-enhanced prompting
• langchain4j-testing-strategies: Prompt validation frameworks
• unit-test-parameterized: Systematic prompt testing approaches

Resources and References

• references/few-shot-patterns.md: Comprehensive few-shot learning frameworks
• references/cot-patterns.md: Chain-of-thought reasoning patterns and examples
• references/optimization-frameworks.md: Systematic prompt optimization methodologies
• references/template-systems.md: Modular template design and implementation
• references/system-prompt-design.md: System prompt architecture and best practices

Usage Examples

Example 1: Classification Task with Few-Shot Learning

Classify customer feedback into categories using semantic similarity for example selection and diversity sampling for edge case coverage.

Example 2: Complex Reasoning with Chain-of-Thought

Implement step-by-step reasoning for financial analysis with verification steps and confidence scoring.

Example 3: Template System for Customer Service

Create modular templates with role-based components and conditional sections for different inquiry types.

Example 4: System Prompt for Code Generation

Design comprehensive system prompt with behavioral guidelines, output requirements, and safety constraints.

Common Pitfalls and Solutions

• Overfitting examples: Use diverse example sets with semantic variety
• Context window overflow: Implement strategic example selection and compression
• Inconsistent outputs: Specify clear output formats and validation requirements
• Poor generalization: Include edge cases and boundary conditions in training examples
• Safety violations: Incorporate comprehensive content policies and harm prevention

Performance Optimization

• Monitor token usage and implement compression strategies
• Use caching for repeated prompt components
• Optimize example selection for maximum learning efficiency
• Implement progressive disclosure for complex prompt systems
• Balance prompt complexity with response quality requirements

Best Practices

Design Principles

• Write clear, specific instructions that leave no ambiguity
• Structure prompts with logical sections and consistent formatting
• Use role-based prompts for consistent behavioral patterns
• Include concrete examples to demonstrate expected outputs
• Define output format requirements explicitly

Implementation Guidelines

• Start with simple prompts and iterate based on testing
• Test with diverse input scenarios including edge cases
• Document prompt versions and their performance metrics
• Implement error handling for unexpected inputs
• Monitor performance in production environments

Common Pitfalls to Avoid

• Overfitting examples to specific patterns
• Ignoring context window limitations and token budgeting
• Neglecting output format specifications
• Failing to handle edge cases and error scenarios
• Not measuring and tracking prompt performance over time

Constraints and Warnings

Model Limitations

• Different models have varying capabilities and token limits
• Complex prompts may exceed context windows unexpectedly
• Model behavior can vary across different versions
• Some reasoning tasks require specific prompting techniques

Resource Considerations

• Longer prompts consume more tokens and increase costs
• Complex few-shot examples impact context usage
• Multiple model calls for optimization increase expenses
• Testing requires sufficient sample sizes for statistical significance

Quality Requirements

• Validate prompts with domain-specific test cases
• Ensure prompts generalize beyond training examples
• Implement monitoring for prompt performance drift
• Regularly review and update prompts as requirements evolve

This skill provides the foundational patterns and methodologies for building production-ready prompt systems that consistently deliver high performance across diverse use cases and model types.