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

•

158

데이터 과학 및 ML

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

소개

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.

주요 기능

Structured JSON output and tool-calling implementations
Prompt chaining and sequential workflow orchestration
Advanced prompting patterns (Few-shot, CoT, ReAct)
Multi-model optimization for Claude, GPT, and Gemini
Cost and latency reduction through prompt caching and token efficiency
158 GitHub stars

사용 사례

Standardizing prompt versioning and cross-model migration workflows
Developing AI agents with consistent tool-use and API interaction capabilities
Building reliable RAG systems for accurate question-answering over documents

소개

주요 기능

Structured JSON output and tool-calling implementations
Prompt chaining and sequential workflow orchestration
Advanced prompting patterns (Few-shot, CoT, ReAct)
Multi-model optimization for Claude, GPT, and Gemini
Cost and latency reduction through prompt caching and token efficiency
158 GitHub stars

사용 사례

Standardizing prompt versioning and cross-model migration workflows
Developing AI agents with consistent tool-use and API interaction capabilities
Building reliable RAG systems for accurate question-answering over documents