Building a Simple RAG Pipeline
Implement a basic RAG workflow from data ingestion to generating responses using a chosen LLM.
Intro to RAG Pipelines
You've learned what RAG is and why it's powerful. Now, let's build one! A Retrieval Augmented Generation (RAG) pipeline is a sequence of steps that combine an LLM with external data.
Its main goal is to give LLMs up-to-date, factual information, reducing "hallucinations" and improving response quality.
Understanding the RAG Flow
Think of a RAG pipeline as having two main phases: preparation and querying. First, you get your data ready. Then, when a user asks a question, your system finds relevant info and uses it to help the LLM answer.
- Preparation: Ingest & Index Data
- Querying: Retrieve & Generate Response
All lessons in this course
- Choosing an LLM Provider
- Data Loading and Text Chunking Basics
- Building a Simple RAG Pipeline
- Testing & Evaluating Your RAG App