Prompt Engineering

name category tags version description

prompt-engineering

backend

prompt-engineering

few-shot-learning

chain-of-thought

optimization

templates

system-prompts

llm-performance

ai-patterns

1.0.0

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.

Prompt Engineering

This skill provides comprehensive frameworks for creating, optimizing, and implementing advanced prompt patterns that significantly improve LLM performance across various tasks and models.

When to Use This Skill

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}

1. 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}

1. 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

1. 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}

1. 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

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.