/prompt-engineering-patterns

name: prompt-engineering-patterns
description: "Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability."
risk: unknown
source: community
date_added: "2026-02-27"

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

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