Build RAG from Scratch

You don't need complex tools to start with RAG. This session builds a surprisingly effective system from scratch using basic vectorization and cosine similarity.

#1about 3 minutes

Why large language models need retrieval augmented generation

Large language models have knowledge cutoffs and lack access to private data, a problem solved by providing relevant context at query time using RAG.

#2about 1 minute

How similarity search and vector embeddings power RAG

RAG relies on similarity search, not keyword search, which captures meaning by converting text into numerical representations called vector embeddings.

#3about 6 minutes

Building a simple bag-of-words vectorizer from scratch

A basic vector embedding can be created by tokenizing text, building a vocabulary of unique words, and representing each document as a vector of word counts.

#4about 8 minutes

Comparing document vectors using cosine similarity

Cosine similarity measures the angle between two vectors to determine their semantic closeness by focusing on direction (meaning) rather than magnitude.

#5about 3 minutes

Understanding the limitations of a bag-of-words model

The simple bag-of-words model is sensitive to vocabulary, slow to scale, and fails to capture nuanced semantic meaning like word order or synonyms.

#6about 4 minutes

Using professional embedding models and vector databases

Production RAG systems use sophisticated embedding models and specialized vector databases for efficient, accurate, and scalable similarity search.

#7about 2 minutes

Exploring advanced RAG techniques and other applications

Beyond basic similarity search, techniques like ColBERT and knowledge graphs can improve retrieval accuracy, and vector search can power features like related content recommendations.