RAG From Scratch
Learn how to build a simple yet powerful RAG System from scratch
When building a RAG (Retrieval-Augmented Generation) system from scratch, it's easy to get carried away with unnecessary complexity. You might be tempted to build everything from the ground up, which could result in a project that never gets finished. Instead, you can create a simple yet effective RAG system using a powerful LLM (Large Language Model) and text embeddings. This is the approach we'll take in this tutorial.
Many people mistakenly believe that RAG systems involve some kind of black magic. They don't. At its core, a RAG system relies on:
- Clever prompt engineering
- Ensuring the system has access to the right context
- Having a great LLM with fast inference speed
In the end, it's about sending a well-crafted prompt (a question combined with relevant context) to the model, which then generates a response.
Tutorial Goals
In this tutorial you will:
- Understand core RAG components
- Build context retriever from scratch using
SQLite-vec - Manage your custom data and add it to the RAG