Prompt Engineering

Design and optimize prompts for large language models (LLMs) to achieve reliable, high-quality outputs across diverse tasks.

Purpose

This skill provides systematic techniques for crafting prompts that consistently elicit desired behaviors from LLMs. Rather than trial-and-error prompt iteration, apply proven patterns (zero-shot, few-shot, chain-of-thought, structured outputs) to improve accuracy, reduce costs, and build production-ready LLM applications. Covers multi-model deployment (OpenAI GPT, Anthropic Claude, Google Gemini, open-source models) with Python and TypeScript examples.

When to Use This Skill

Trigger this skill when:

• Building LLM-powered applications requiring consistent outputs
• Model outputs are unreliable, inconsistent, or hallucinating
• Need structured data (JSON) from natural language inputs
• Implementing multi-step reasoning tasks (math, logic, analysis)
• Creating AI agents that use tools and external APIs
• Optimizing prompt costs or latency in production systems
• Migrating prompts across different model providers
• Establishing prompt versioning and testing workflows

Common requests:

• "How do I make Claude/GPT follow instructions reliably?"
• "My JSON parsing keeps failing - how to get valid outputs?"
• "Need to build a RAG system for question-answering"
• "How to reduce hallucination in model responses?"
• "What's the best way to implement multi-step workflows?"

Quick Start

Zero-Shot Prompt (Python + OpenAI):

from openai import OpenAI
client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Summarize this article in 3 sentences: [text]"}
    ],
    temperature=0  # Deterministic output
)
print(response.choices[0].message.content)

Structured Output (TypeScript + Vercel AI SDK):

import { generateObject } from 'ai';
import { openai } from '@ai-sdk/openai';
import { z } from 'zod';

const schema = z.object({
  name: z.string(),
  sentiment: z.enum(['positive', 'negative', 'neutral']),
});

const { object } = await generateObject({
  model: openai('gpt-4'),
  schema,
  prompt: 'Extract sentiment from: "This product is amazing!"',
});

Prompting Technique Decision Framework

Choose the right technique based on task requirements:

Decision tree:

START
├─ Need structured JSON? → Use JSON Mode / Tool Calling (references/structured-outputs.md)
├─ Complex reasoning required? → Use Chain-of-Thought (references/chain-of-thought.md)
├─ Specific format/style needed? → Use Few-Shot Learning (references/few-shot-learning.md)
├─ Knowledge from documents? → Use RAG (references/rag-patterns.md)
├─ Multi-step workflow? → Use Prompt Chaining (references/prompt-chaining.md)
├─ Agent with tools? → Use Tool Use / ReAct (references/tool-use-guide.md)
└─ Simple task → Use Zero-Shot (references/zero-shot-patterns.md)

Core Prompting Patterns

1. Zero-Shot Prompting

Pattern: Clear instruction + optional context + input + output format specification

When to use: Simple, well-defined tasks with clear expected outputs (summarization, translation, basic classification).

Best practices:

• Be specific about constraints and requirements
• Use imperative voice ("Summarize...", not "Can you summarize...")
• Specify output format upfront
• Set temperature=0 for deterministic outputs

Example:

prompt = """
Summarize the following customer review in 2 sentences, focusing on key concerns:

Review: [customer feedback text]

Summary:
"""

See references/zero-shot-patterns.md for comprehensive examples and anti-patterns.

2. Chain-of-Thought (CoT)

Pattern: Task + "Let's think step by step" + reasoning steps → answer

When to use: Complex reasoning tasks (math problems, multi-hop logic, analysis requiring intermediate steps).

Research foundation: Wei et al. (2022) demonstrated 20-50% accuracy improvements on reasoning benchmarks.

Zero-shot CoT:

prompt = """
Solve this problem step by step:

A train leaves Station A at 2 PM going 60 mph.
Another leaves Station B at 3 PM going 80 mph.
Stations are 300 miles apart. When do they meet?

Let's think through this step by step:
"""

Few-shot CoT: Provide 2-3 examples showing reasoning steps before the actual task.

See references/chain-of-thought.md for advanced patterns (Tree-of-Thoughts, self-consistency).

3. Few-Shot Learning

Pattern: Task description + 2-5 examples (input → output) + actual task

When to use: Need specific formatting, style, or classification patterns not easily described.

Sweet spot: 2-5 examples (quality > quantity)

Example structure:

prompt = """
Classify sentiment of movie reviews.

Examples:
Review: "Absolutely fantastic! Loved every minute."
Sentiment: positive

Review: "Waste of time. Terrible acting."
Sentiment: negative

Review: "It was okay, nothing special."
Sentiment: neutral

Review: "{new_review}"
Sentiment:
"""

Best practices:

• Use diverse, representative examples
• Maintain consistent formatting
• Randomize example order to avoid position bias
• Label edge cases explicitly

See references/few-shot-learning.md for selection strategies and common pitfalls.

4. Structured Output Generation

Modern approach (2025): Use native JSON modes and tool calling instead of text parsing.

OpenAI JSON Mode:

from openai import OpenAI
client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "Extract user data as JSON."},
        {"role": "user", "content": "From bio: 'Sarah, 28, sarah@example.com'"}
    ],
    response_format={"type": "json_object"}
)

Anthropic Tool Use (for structured outputs):

import anthropic
client = anthropic.Anthropic()

tools = [{
    "name": "record_data",
    "description": "Record structured user information",
    "input_schema": {
        "type": "object",
        "properties": {
            "name": {"type": "string"},
            "age": {"type": "integer"}
        },
        "required": ["name", "age"]
    }
}]

message = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    tools=tools,
    messages=[{"role": "user", "content": "Extract: 'Sarah, 28'"}]
)

TypeScript with Zod validation:

import { generateObject } from 'ai';
import { z } from 'zod';

const schema = z.object({
  name: z.string(),
  age: z.number(),
});

const { object } = await generateObject({
  model: openai('gpt-4'),
  schema,
  prompt: 'Extract: "Sarah, 28"',
});

See references/structured-outputs.md for validation patterns and error handling.

5. System Prompts and Personas

Pattern: Define consistent behavior, role, constraints, and output format.

Structure:

1. Role/Persona
2. Capabilities and knowledge domain
3. Behavior guidelines
4. Output format constraints
5. Safety/ethical boundaries

Example:

sys