Introduction to RAG

What is RAG?

RAG (Retrieval-Augmented Generation) is a powerful AI technique that combines the best of both worlds: information retrieval and text generation.

Traditional language models generate responses based solely on their training data, which can be outdated or incomplete. RAG solves this by:

• Retrieving relevant information from external knowledge sources
• Augmenting the model's context with this retrieved information
• Generating accurate and up-to-date responses

Why RAG?

RAG addresses several limitations of traditional LLMs:

• Knowledge Cutoff: LLMs have a training cutoff date. RAG allows access to current information.
• Domain-Specific Knowledge: RAG can incorporate specialized knowledge from documents, databases, or APIs.
• Accuracy: By retrieving factual information, RAG reduces hallucinations and improves answer quality.
• Transparency: RAG systems can cite sources, making responses more trustworthy.

How RAG Works

The RAG workflow consists of two main phases:

1. Indexing Phase

1. Collect and preprocess documents
2. Split documents into chunks
3. Generate embeddings for each chunk
4. Store embeddings in a vector database

2. Query Phase

1. User asks a question
2. Generate embedding for the query
3. Search vector database for similar chunks
4. Retrieve top-k relevant chunks
5. Pass chunks as context to LLM
6. Generate final response

Use Cases

• Question-answering systems
• Document analysis and summarization
• Chatbots with domain knowledge
• Code documentation assistants
• Research assistants