prompt-engineering-patterns

What this skill does

# Prompt Engineering Patterns

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

## When to Use This Skill

- 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
- Using structured outputs (JSON mode) for reliable parsing

## 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. Structured Outputs

- JSON mode for reliable parsing
- Pydantic schema enforcement
- Type-safe response handling
- Error handling for malformed outputs

### 4. 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

### 5. Template Systems

- Variable interpolation and formatting
- Conditional prompt sections
- Multi-turn conversation templates
- Role-based prompt composition
- Modular prompt components

### 6. 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 langchain_anthropic import ChatAnthropic
from langchain_core.prompts import ChatPromptTemplate
from pydantic import BaseModel, Field

# Define structured output schema
class SQLQuery(BaseModel):
    query: str = Field(description="The SQL query")
    explanation: str = Field(description="Brief explanation of what the query does")
    tables_used: list[str] = Field(description="List of tables referenced")

# Initialize model with structured output
llm = ChatAnthropic(model="claude-sonnet-4-6")
structured_llm = llm.with_structured_output(SQLQuery)

# Create prompt template
prompt = ChatPromptTemplate.from_messages([
    ("system", """You are an expert SQL developer. Generate efficient, secure SQL queries.
    Always use parameterized queries to prevent SQL injection.
    Explain your reasoning briefly."""),
    ("user", "Convert this to SQL: {query}")
])

# Create chain
chain = prompt | structured_llm

# Use
result = await chain.ainvoke({
    "query": "Find all users who registered in the last 30 days"
})
print(result.query)
print(result.explanation)
```

## Key Patterns

### Pattern 1: Structured Output with Pydantic

```python
from anthropic import Anthropic
from pydantic import BaseModel, Field
from typing import Literal
import json

class SentimentAnalysis(BaseModel):
    sentiment: Literal["positive", "negative", "neutral"]
    confidence: float = Field(ge=0, le=1)
    key_phrases: list[str]
    reasoning: str

async def analyze_sentiment(text: str) -> SentimentAnalysis:
    """Analyze sentiment with structured output."""
    client = Anthropic()

    message = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=500,
        messages=[{
            "role": "user",
            "content": f"""Analyze the sentiment of this text.

Text: {text}

Respond with JSON matching this schema:
{{
    "sentiment": "positive" | "negative" | "neutral",
    "confidence": 0.0-1.0,
    "key_phrases": ["phrase1", "phrase2"],
    "reasoning": "brief explanation"
}}"""
        }]
    )

    return SentimentAnalysis(**json.loads(message.content[0].text))
```

### Pattern 2: Chain-of-Thought with Self-Verification

```python
from langchain_core.prompts import ChatPromptTemplate

cot_prompt = ChatPromptTemplate.from_template("""
Solve this problem step by step.

Problem: {problem}

Instructions:
1. Break down the problem into clear steps
2. Work through each step showing your reasoning
3. State your final answer
4. Verify your answer by checking it against the original problem

Format your response as:
## Steps
[Your step-by-step reasoning]

## Answer
[Your final answer]

## Verification
[Check that your answer is correct]
""")
```

### Pattern 3: Few-Shot with Dynamic Example Selection

```python
from langchain_voyageai import VoyageAIEmbeddings
from langchain_core.example_selectors import SemanticSimilarityExampleSelector
from langchain_chroma import Chroma

# Create example selector with semantic similarity
example_selector = SemanticSimilarityExampleSelector.from_examples(
    examples=[
        {"input": "How do I reset my password?", "output": "Go to Settings > Security > Reset Password"},
        {"input": "Where can I see my order history?", "output": "Navigate to Account > Orders"},
        {"input": "How do I contact support?", "output": "Click Help > Contact Us or email [email protected]"},
    ],
    embeddings=VoyageAIEmbeddings(model="voyage-3-large"),
    vectorstore_cls=Chroma,
    k=2  # Select 2 most similar examples
)

async def get_few_shot_prompt(query: str) -> str:
    """Build prompt with dynamically selected examples."""
    examples = await example_selector.aselect_examples({"input": query})

    examples_text = "\n".join(
        f"User: {ex['input']}\nAssistant: {ex['output']}"
        for ex in examples
    )

    return f"""You are a helpful customer support assistant.

Here are some example interactions:
{examples_text}

Now respond to this query:
User: {query}
Assistant:"""
```

### Pattern 4: Progressive Disclosure

Start with simple prompts, add complexity only when needed:

```python
PROMPT_LEVELS = {
    # Level 1: Direct instruction
    "simple": "Summarize this article: {text}",

    # Level 2: Add constraints
    "constrained": """Summarize this article in 3 bullet points, focusing on:
- Key findings
- Main conclusions
- Practical implications

Article: {text}""",

    # Level 3: Add reasoning
    "reasoning": """Read this article carefully.
1. First, identify the main topic and thesis
2. Then, extract the key supporting points
3. Finally, summarize in 3 bullet points

Article: {text}

Summary:""",

    # Level 4: Add examples
    "few_shot": """Read articles and provide concise summaries.

Example:
Article: "New research shows that regular exercise can reduce anxiety by up to 40%..."
Summary:
• Regular exercise reduces anxiety by up to 40%
• 30 minutes of moderate activity 3x/week is sufficient
• Benefits appear within 2 weeks of starting

Now summarize this article:
Article: {text}

Summary:"""
}
```

### Pattern 5: Error Recovery and Fallback

```python
from pydantic import BaseModel, ValidationError
import json

class ResponseWithConfidence(BaseModel):
    answer: str
    confidence: float
    sources: list[str]
    alternative_interpretations: list[str] = []

ERROR_RECOVERY_PROMPT = """
Answer the question based on the context provided.

Context: {context}
Question: {question}

Instructions:
1. If you can answer confidently (>0.8), provide a direct answer
2. If you're somewhat confident (0.5-0.8), provide your best answer with caveats
3. If you're uncertain (<0.5), explain what information is missing
4. Always provide alternative interpretations if the question is ambiguous

Respond in JSON:
{{
    "answer": "your answer or 'I cannot determine this from the context'",
    "confidence"