Retrieval Augmented Generation (RAG) Patterns #

Retrieval Augmented Generation (RAG) is a technique that enhances generative AI outputs by first retrieving relevant information from a knowledge base and then using that information to guide the generation process.

Why RAG? #

RAG addresses several limitations of foundation models:

1. Knowledge Cutoffs: Foundation models have knowledge limited to their training data
2. Hallucinations: Models may generate plausible but incorrect information
3. Sourcing: RAG can provide attribution for generated content
4. Customization: Allows models to access domain-specific knowledge without fine-tuning

Basic RAG Architecture #

1. Document Processing: Convert documents into chunks and embeddings
2. Retrieval: Find relevant information based on the query
3. Augmented Prompting: Enhance the prompt with retrieved information
4. Generation: Produce the final output using the augmented prompt

Patterns in this Section #

• Basic RAG Implementation
• Advanced Retrieval Strategies
• RAG with Document Chunking
• Multi-step RAG