RAG (Retrieval-Augmented Generation)

Overview

RAG (Retrieval-Augmented Generation) enhances LLMs by retrieving relevant information from external knowledge bases before generating responses.

How RAG Works

1. Indexing Phase

• Split documents into chunks
• Generate embeddings
• Store in vector database

2. Retrieval Phase

• Convert query to embedding
• Search vector database
• Retrieve top-k relevant chunks

3. Generation Phase

• Combine query + retrieved context
• Send to LLM
• Generate grounded response

Benefits

• Reduces Hallucinations: Grounds responses in facts
• Current Information: No retraining needed
• Citations: Can reference sources
• Domain Expertise: Access specialized knowledge
• Cost-Effective: vs fine-tuning LLMs

Components

• Document Store: Original documents
• Vector Database: Embedded chunks
• Embedding Model: Text to vectors
• LLM: Response generation
• Orchestration: Pipeline management

Best Practices

• Quality chunking strategies
• Good embedding models
• Hybrid search (keyword + semantic)
• Reranking for precision
• Contextual retrieval techniques

Popular Frameworks

• LangChain
• LlamaIndex
• Haystack
• Semantic Kernel

Use Cases

• Customer support chatbots
• Document Q&A
• Knowledge management
• Research assistants
• Internal documentation

Pricing

Technique, costs from vector DB + LLM + embeddings.