rag-design-patterns

What problem does it solve?

This Skill provides a comprehensive guide to building effective Retrieval Augmented Generation (RAG) systems, addressing challenges like irrelevant retrieval, poor context quality, and inefficient vector search. It ensures your LLMs have access to the most relevant and accurate information, significantly improving response quality.

Core Features & Use Cases

• Document Chunking: Guides on semantic chunking strategies with overlap to preserve context and optimize retrieval.
• Vector Database Integration: Patterns for integrating with vector stores, adding chunks with embeddings, and performing efficient semantic searches.
• Reranking & Query Expansion: Implements reranking for improved precision and query rewriting/expansion to enhance recall.
• Context Assembly: Provides strategies for building concise and token-limited contexts from retrieved chunks for LLM consumption.
• Use Case: A data scientist is building a chatbot that answers questions based on a large knowledge base. This skill helps them implement semantic chunking, integrate with a vector database, use reranking to prioritize relevant documents, and assemble a concise context for the LLM, leading to more accurate answers.

Quick Start

Implement a semantic chunking strategy for a document, ensuring chunks have appropriate overlap and metadata.