# Prompt Engineering Patterns

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

- Skill: `sickn33/prompt-engineering-patterns` (Agent Skill)
- Install (CLI): `npx skillmds@latest add sickn33/prompt-engineering-patterns`
- Raw SKILL.md: https://api.skillmd.com/api/skills/sickn33/prompt-engineering-patterns/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: sickn33 (https://skillmd.com/u/sickn33)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/sickn33/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

```python
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
```python
# Combine retrieved context with prompt engineering
prompt = f"""Given the following context:
{retrieved_context}

{few_shot_examples}

Question: {user_question}

Provide a detailed a