Prompt Engineering Patterns

# 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**