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Results for "RAG"

  1. 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.

  2. 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.

  3. Article · AI security

    Prompt injection in RAG systems and how to reduce it

    Prompt injection makes a language model follow attacker-written text. Learn direct and indirect injection in RAG, why filters fail, and layered defences.

  4. Article · Retrieval-augmented generation

    How to evaluate a RAG system: retrieval and answer quality

    Measure a RAG system in two layers: did retrieval find the right passages, and is the answer faithful to them? Learn recall@k, MRR, faithfulness and a practical test set.

  5. 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.

  6. 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.

  7. Article · Machine learning methods

    Overfitting and underfitting in machine learning

    Overfitting means a model memorises its training data; underfitting means it misses the pattern. Learn to spot both from your scores and how to fix each.

  8. Article · LLM foundations

    What is a context window in an LLM?

    The context window is the maximum number of tokens a language model can consider at once. Learn what fills it, what happens when it overflows, and how to budget it.

  9. Article · Machine learning methods

    Data leakage in machine learning: how good scores lie

    Data leakage lets outside information slip into training, inflating scores that collapse in production. Learn the common types and how to prevent them.

  10. Article · LLM foundations

    Why long context windows still miss things

    A million-token context window does not mean a model uses every token well. Learn about the lost-in-the-middle effect, attention cost, and how to test it yourself.