RAG Explained: Retrieval Augmented Generation

RAG Explained: Retrieval Augmented Generation is an essential skill for modern operators. This guide covers everything you need to get started.

What You Need to Know

RAG (Retrieval-Augmented Generation) solves the fundamental limitation of LLMs — they only know what they were trained on. RAG lets any LLM answer questions about your specific, private, up-to-date data.

The RAG pipeline works in two phases: indexing (chunking your documents, generating embeddings, storing in a vector database) and retrieval (embedding the query, finding similar chunks, injecting them as context for the LLM).

Operators who implement RAG build AI applications that answer questions about their business documents, product knowledge bases, customer data, and proprietary information with up-to-date accuracy.

Getting Started: Step by Step

1. Understand the RAG architecture — Map out the full pipeline: document ingestion → chunking → embedding → vector storage → query → retrieval → LLM generation.
2. Choose your embedding model — Select an embedding model (OpenAI text-embedding-3-small, Cohere Embed, or local models) based on cost and quality needs.
3. Set up your vector database — Choose and configure a vector database (Chroma for local, Pinecone or Qdrant for production) for embedding storage.
4. Build the ingestion pipeline — Write code to load documents, split into chunks, generate embeddings, and store them in your vector database.
5. Implement the retrieval and generation step — Embed the user query, retrieve top-k similar chunks, and inject them into the LLM prompt as context.

Key Tools

• LlamaIndex — Python framework that handles the entire RAG pipeline from ingestion to query with minimal configuration.
• Chroma — Open-source vector database for local RAG development with a simple Python API.
• Pinecone — Managed vector database for production RAG applications with high-scale retrieval.

Next Steps

The best way to go deeper is to join fellow operators at skool.com/aiguerrilla — a free community where hundreds of practitioners share what's actually working.