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

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

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

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

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

Performance Analysis

• Measure current prompt performance metrics

• Identify failure modes and error patterns

• Analyze token efficiency and response latency

• Assess consistency across multiple runs

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

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

Modular Architecture Design

• Decompose complex prompts into reusable components

• Create template inheritance hierarchies

• Implement dynamic example selection systems

• Build automated quality assurance frameworks

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.