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Learning Series 05

RAG

Understand retrieval-augmented generation end to end: embeddings, vector search, ranking, grounding and evaluation.

05
Topics
10m
Reading time
Intermediate
Level
Overview

RAG is the most common way to put a language model to work on your own content. This series covers each stage of the pipeline and how to evaluate it.

Read LLM Basics first, particularly context windows and embeddings. References include the original RAG research and evaluation work.

What You’ll Learn

The problem being solved

LLMs lack private and recent knowledge and may hallucinate. Retrieval-augmented generation, introduced by Lewis et al., retrieves relevant passages and supplies them to the model, so answers are grounded, citable and updatable without retraining.

In simple terms

Instead of hoping the model remembers, look the answer up and hand it over.

Key concepts
Real-world example

Enterprise assistants use RAG to answer from policy documents and knowledge bases, citing the source passage with each answer.

References
Lewis et al. (2020) — Retrieval-Augmented GenerationKarpukhin et al. (2020) — Dense Passage Retrieval
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06

Machine Learning

Understand machine learning concepts, model development, training, evaluation, and practical applications.

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Understanding Retrieval-Augmented Generation

A practical guide to grounding a model in your own content.