embeddings

Candidate generation's workhorse is the two-tower model: one network embeds users, another embeds items, and relevance becomes a dot product in shared space. This lesson traces the idea from collaborative filtering through matrix factorization to trained towers, why the towers must never mix features, and the cold-start problem geometry alone cannot solve.

Vector search did not replace keyword search, because the two fail in opposite places: BM25 cannot see that laptop and notebook mean the same thing, and embeddings cannot see that SKU-4471-B is not approximately anything. This lesson maps the two failure surfaces, then builds the production answer: run both retrievers and fuse the rankings, with reciprocal rank fusion done by hand.

A query can only ask about what is recorded, so the harder half of curation is generating fields that surface where to look. This lesson covers the analysis methods FiftyOne bundles as the Brain: embedding visualisation and its four reduction methods, uniqueness and representativeness, mistakenness and hardness, and similarity indexes for near-duplicate detection and text search.

Vector search exists because exact nearest neighbour search is intractable at scale and the curse of dimensionality defeats the classical index structures. This lesson covers distance metrics and when each is right, why brute force costs what it does, why k-d trees fail above a few dozen dimensions, and the recall-latency trade that every approximate index makes.

Why a brand new post can reach millions on one platform and nothing on another. Learn the cold-start problem and why collaborative filtering cannot solve it alone, how content embeddings let a system rank an item nobody has touched, the difference between a social graph and an interest graph, and what actually separates YouTube, TikTok, Instagram and Pinterest.

Every feed has the same impossible job: pick ten items out of millions, in under a tenth of a second. The answer is a funnel. Learn why recommenders split into a cheap retrieval stage and an expensive ranking stage, how two-tower models make retrieval possible, and why nearest-neighbour search is the trick that makes the whole thing fit in a budget.

Learn how to systematically evaluate Retrieval-Augmented Generation systems in production using RAGAS, TruLens, and Phoenix — covering golden sets, retrieval drift, embedding drift, and cost-aware eval scheduling.

Deep dive into two advanced RAG architectures — HippoRAG's hippocampal-inspired knowledge graph indexing and RAPTOR's recursive summarization tree — and why both dramatically outperform flat vector retrieval on multi-hop questions.

Master four advanced query rewriting techniques that dramatically improve RAG retrieval quality: Hypothetical Document Embeddings, multi-query expansion, query decomposition, and step-back prompting. Learn when to reach for each and how to implement them.

Build a production-grade retrieval pipeline combining BM25 keyword search with dense vector search, then sharpen precision with cross-encoder rerankers (Cohere Rerank, Voyage rerank-2, BGE). Learn when each layer matters and how to wire them together.

A deep dive into how you split documents for retrieval-augmented generation — fixed-size, recursive, semantic, hierarchical, and late chunking — with concrete trade-offs and code for each approach.

OpenAI released CLIP. LAION and friends released OpenCLIP — a reproducible, openly-trained re-implementation that has quietly become the default vision-language embedding backbone. Here's what it is, why it won, and how to drop it into a project.

How models like CLIP, GPT-4V, and Claude visual learn to talk about pictures. Cover the contrastive trick behind CLIP, the difference between embedding models and generative VLMs, and where each one shines.

Dive into the world of vector databases, specialized systems designed to store and query high-dimensional vector embeddings efficiently. Learn how these databases power semantic search, recommendation systems, and large language model applications by finding semantically similar data points at scale.