How does prompt engineering affect API costs?

It offers strategies for prompt caching and token optimization, helping you achieve high-quality results with fewer tokens and reduced latency.

What models does this skill support?

It provides techniques and code examples compatible with all major LLMs including Anthropic Claude, OpenAI GPT-4, Google Gemini, and various open-source models.

How does this skill help reduce model hallucinations?

By implementing systematic techniques like Chain-of-Thought (CoT) reasoning and providing few-shot examples, the skill guides models through logical steps, significantly improving accuracy.

Can I use this for generating valid JSON data?

Yes, it includes specialized patterns for Structured Output generation, JSON Mode, and tool-calling to ensure outputs are always parseable by your application.

Does this support popular AI frameworks?

The skill provides implementation patterns for industry-standard tools including LangChain, LlamaIndex, the Vercel AI SDK, and native model provider SDKs.

Prompt Engineering

Name: Prompt Engineering
Author: ancoleman

byancoleman

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158

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Data Science & ML

Engineers reliable, high-quality LLM outputs using advanced techniques like few-shot learning, chain-of-thought reasoning, and structured data patterns.

This skill provides a systematic framework for designing and optimizing prompts to move beyond trial-and-error development. It equips developers with proven patterns—including zero-shot, few-shot, chain-of-thought, and structured JSON outputs—to build production-ready AI applications. Whether you are implementing RAG systems, creating complex AI agents, or optimizing for cost and latency, this skill offers multi-model guidance for Anthropic, OpenAI, and open-source ecosystems using Python and TypeScript.

Key Features

01Structured JSON output and tool-calling implementations

02Prompt chaining and sequential workflow orchestration

03Advanced prompting patterns (Few-shot, CoT, ReAct)

04Multi-model optimization for Claude, GPT, and Gemini

05Cost and latency reduction through prompt caching and token efficiency

06158 GitHub stars

Use Cases

01Standardizing prompt versioning and cross-model migration workflows

02Developing AI agents with consistent tool-use and API interaction capabilities

03Building reliable RAG systems for accurate question-answering over documents

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add ancoleman/ai-design-components prompt-engineering

For use in Claude.ai and ChatGPT

Download Skill