Getting Started with RAG: A Beginner's Guide

Retrieval-Augmented Generation (RAG) is one of the most practical ways to make LLMs more useful for your specific use case.

What is RAG?

RAG combines the power of large language models with your own data. Instead of relying solely on what the model was trained on, RAG retrieves relevant documents and feeds them to the LLM as context.

Key Components

1. Document Loading

First, you need to load and parse your documents into text chunks.

2. Embedding

Convert your text chunks into vector embeddings using a model like OpenAI's text-embedding-ada-002 or open-source alternatives.

3. Vector Store

Store your embeddings in a vector database like ChromaDB, Pinecone, or Qdrant.

4. Retrieval

When a user asks a question, embed their query and find the most similar chunks in your vector store.

5. Generation

Pass the retrieved chunks as context to your LLM along with the user's question.

Getting Started

Check out our upcoming meetup on RAG applications where we'll build one from scratch!