Retrieval-Augmented Generation (RAG) Overview

RAG is a technique that combines information retrieval with text generation to
produce more accurate, grounded, and up-to-date responses from language models.

How RAG Works:

1. Indexing Phase
   - Documents are split into chunks
   - Each chunk is converted to embeddings (vectors)
   - Embeddings are stored in a vector database

2. Retrieval Phase
   - User query is converted to an embedding
   - Similar documents are retrieved via vector similarity
   - Top-k most relevant chunks are selected

3. Generation Phase
   - Retrieved context is combined with the query
   - LLM generates a response using the context
   - Response includes information from source documents

Benefits of RAG:
- Reduces hallucinations by grounding responses in facts
- Enables access to private/proprietary data
- Keeps information current without retraining
- Provides source attribution for answers

RAG vs Fine-tuning:
- RAG: Better for factual Q&A, frequently changing data
- Fine-tuning: Better for style/format, specialized domains
- Hybrid: Combine both for best results

Advanced RAG Techniques:
- Hybrid search: Combine semantic and keyword search
- Re-ranking: Use cross-encoders to refine results
- Query expansion: Generate multiple query variations
- Hierarchical indexing: Multi-level document organization

Common Challenges:
- Chunk size selection affects retrieval quality
- Embedding model choice impacts semantic understanding
- Context window limits constrain retrieved information
- Latency increases with larger knowledge bases
