Tagged "embeddings"
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.
Retrieval-augmented generationIntermediate3 minHow 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.
Retrieval-augmented generationIntermediate3 minWhat 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.
LLM foundationsBeginner3 minWhat 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.
Retrieval-augmented generationBeginner3 min