@tank/llm-app-patterns

What problem does it solve?

This Skill helps developers build robust, production-ready applications powered by Large Language Models, addressing common challenges in areas like data retrieval, agentic behavior, and user experience.

Core Features & Use Cases

• RAG Systems: Implement effective Retrieval-Augmented Generation for accurate, context-aware responses.
• Tool-Using Agents: Design and deploy agents that can leverage external tools and APIs.
• Structured Output: Ensure LLMs return data in predictable, machine-readable formats.
• Streaming Responses: Improve user experience with real-time output delivery.
• Cost Optimization: Strategies to manage and reduce LLM inference costs.
• Use Case: Integrate a RAG system into your customer support chatbot to provide accurate answers based on your company's knowledge base, or build an agent that can schedule meetings by calling calendar APIs.

Quick Start

Use the llm-app-patterns skill to implement a RAG system that answers questions from a provided document.