Results for "embeddings"
Article · LLM foundations
What are embeddings? Meaning as numbers, explained
Embeddings turn text into lists of numbers so that similar meanings land close together. Learn how they work, how similarity is measured, and where they fail.
Learning path · Learning path: AI
How language models read text
Tokens, context windows and embeddings: what a model actually receives.
Article · Retrieval-augmented generation
How vector search works: similarity, top-k and hybrid search
Vector search finds text by meaning instead of exact words. Learn cosine similarity, top-k retrieval, approximate nearest neighbour indexes, BM25 and hybrid ranking.
Lab · Retrieval-augmented generation
Build a tiny RAG system in Python
Build a working retrieval-augmented generation pipeline in about 60 lines of Python: chunk notes, retrieve with TF-IDF, build a grounded prompt, and inspect every step.
Article · LLM foundations
What is an LLM (Large Language Model)? A Complete Beginner-to-Builder Guide
A beginner-to-builder guide to large language models: next-token prediction, the transformer and attention, how models are trained, and how to start a career.
Project · Project
An AI tutor that shows its retrieval
The tutor on this site is a small, inspectable RAG system: heading-aware chunking, BM25 or hybrid retrieval, grounded prompts and visible sources.
Article · Retrieval-augmented generation
How to chunk documents for RAG
Chunking decides what your RAG system can retrieve. Compare fixed-size, recursive, heading-aware and semantic chunking, and learn how to pick size and overlap.
Article · Retrieval-augmented generation
What is retrieval-augmented generation (RAG)?
RAG makes a language model answer from your own documents by retrieving relevant passages first. Learn the pipeline step by step, and when RAG is the right choice.