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Results for "prompt injection"

  1. Tool · AI security

    Prompt injection checker

    See which common injection patterns a simple filter spots, and learn why filters are not enough.

  2. 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.

  3. Learning path · Learning path: AI

    Build and evaluate RAG systems

    Chunking, retrieval, grounded prompts, evaluation and security for answering from your own documents.

  4. Project · Project

    An AI tutor that shows its retrieval

    The tutor on this site is a small, inspectable RAG system: heading-aware chunking, BM25 or hybrid retrieval, grounded prompts and visible sources.

  5. Lab · Retrieval-augmented generation

    Build a tiny RAG system in Python

    Build a working retrieval-augmented generation pipeline in about 60 lines of Python: chunk notes, retrieve with TF-IDF, build a grounded prompt, and inspect every step.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. Article · LLM foundations

    What is a token in an LLM?

    A token is the unit of text a language model reads and writes. Learn how tokenizers split text, why token counts differ by language, and why it matters for cost.