prompt-engineering-patterns

•

6551216 months ago

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.

Quick Install

npxskills add rmyndharis/antigravity-skills--skill prompt-engineering-patterns

Instructions

Prompt Engineering Patterns

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.

Do not use this skill when

The task is unrelated to prompt engineering patterns
You need a different domain or tool outside this scope

Instructions

Clarify goals, constraints, and required inputs.
Apply relevant best practices and validate outcomes.
Provide actionable steps and verification.

Use this skill when

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

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Prompt Engineering Patterns

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.

Do not use this skill when

The task is unrelated to prompt engineering patterns
You need a different domain or tool outside this scope

Instructions

Clarify goals, constraints, and required inputs.
Apply relevant best practices and validate outcomes.
Provide actionable steps and verification.

Use this skill when

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