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Results for "embeddings"

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.