llm-app-development

What problem does it solve?

This Skill provides a comprehensive framework and best practices for developing robust, scalable, and production-ready applications powered by Large Language Models (LLMs).

Core Features & Use Cases

• RAG Pipelines: Implement Retrieval-Augmented Generation for grounding LLM responses in external knowledge.
• Structured Output: Ensure reliable, typed data output from LLMs using schemas like Zod.
• Agentic Workflows: Enable LLMs to use tools and perform actions autonomously.
• Cost Optimization: Employ semantic caching and model selection strategies to manage expenses.
• Streaming Responses: Improve user experience with real-time token delivery.
• Evaluation Frameworks: Implement rigorous testing and scoring for LLM features.
• Use Case: Develop an AI customer support chatbot that can access a knowledge base, answer user queries accurately, and escalate complex issues by creating support tickets.

Quick Start

Use the llm-app-development skill to implement a RAG pipeline for a given set of documents and a user query.