Jeroen Overschie - The Levels of RAG 🦜

RAG (Retrieval Augmented Generation) enhances LLM responses by first retrieving relevant documents before generating answers, enabling access to up-to-date knowledge and internal company data

Four progressive levels of RAG implementation:

• Level 1: Basic RAG with vector search and chunking
• Level 2: Hybrid search combining vector search with keyword search (tf-idf, BM25)
• Level 3: Advanced data format handling (PDFs, tables, structured data)
• Level 4: Multimodal capabilities (images, audio, video)

Proper chunking is critical for effective RAG:

• Chunk size should maintain semantic meaning
• Consider document structure and natural breaks
• Avoid splitting words or losing context
• Requires experimentation and evaluation

Vector databases are essential for RAG:

• Store document embeddings for similarity search
• Enable semantic matching between queries and documents
• Support hybrid search capabilities
• Can handle multimodal content

Common challenges and solutions:

• PDF parsing requires specialized tools and formats
• Tables need special treatment (computer vision or markdown conversion)
• Cost management with multimodal models
• Hallucination prevention through proper context

Production considerations:

• Need robust evaluation systems
• Consider costs vs. benefits of different models
• Monitor system performance
• Test with representative datasets
• May need to move beyond framework abstractions for stability

RAG gained massive popularity after ChatGPT’s launch, despite the original paper being from 2020

Citations and evidence tracking are important for building user trust and verifying responses