10 posts · page 1 of 1
Understand how AI embeddings work, from text to dense vector representations. Learn to generate, compare, and use embeddings for search, clustering, and classification.
Compare Pinecone, Weaviate, and Chroma vector databases for RAG applications. Covers setup, performance, pricing, and when to use each one.
A practical introduction to vector search with FAISS: how indexes work, which index to pick, and how to wire it into a real retrieval pipeline for embeddings.
Learn how chunk overlap rescues boundary context in RAG pipelines, with practical strategies for choosing overlap size and shape for different corpora.
Compare fixed-size, sentence, semantic, and structural chunking for retrieval augmented generation and pick the right one for your corpus.
Combine lexical BM25 with dense vector search to recover the queries each method misses on its own and boost RAG retrieval quality.
Learn how Hypothetical Document Embeddings (HyDE) improve RAG recall by embedding a generated answer instead of the raw query, with examples and trade-offs.
What embeddings are, why they work, how to use them for search and clustering, how to pick a model, and the practical pitfalls that bite first-time users.
A practical tutorial on text embeddings: vector intuition, calling an embeddings API, choosing dimensions, computing cosine similarity, and caching the results.
What an embedding is, why cosine similarity works, how dimensionality and chunking choices affect retrieval, and a tiny numpy example you can run in your head.