RAG Foundations

Retrieval-Augmented Generation (RAG) is the architecture of choice for building AI applications that need to answer questions based on specific, private, or up-to-date data.

This skill is a meta-guide that shows you how to combine other specialized skills in this library to build a production-grade RAG pipeline.

The Architecture

A standard RAG pipeline has five distinct stages:

1. Ingestion ("ETL"): Loading and cleaning your data.
2. Embedding: Converting text into vector representations.
3. Storage: Saving vectors in a database for fast similarity search.
4. Retrieval: Finding the most relevant context for a user query.
5. Generation: Using an LLM to synthesize an answer from the retrieved context.

Skill Stack

To build this, you will combine the following skills: