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.

●If detailed examples are required, open resources/implementation-playbook.md.

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:

1.Level 1: Direct instruction

●"Summarize this article"

2.Level 2: Add constraints

●"Summarize this article in 3 bullet points, focusing on key findings"

3.Level 3: Add reasoning

●"Read this article, identify the main findings, then summarize in 3 bullet points"

4.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

1.Be Specific: Vague prompts produce inconsistent results

2.Show, Don't Tell: Examples are more effective than descriptions

3.Test Extensively: Evaluate on diverse, representative inputs

4.Iterate Rapidly: Small changes can have large impacts

5.Monitor Performance: Track metrics in production

6.Version Control: Treat prompts as code with proper versioning

7.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

1.Review the prompt template library for common patterns

2.Experiment with few-shot learning for your specific use case

3.Implement prompt versioning and A/B testing

4.Set up automated evaluation pipelines

5.Document your prompt engineering decisions and learnings

prompt-engineering-patterns

Documentation