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)