Learn AI concepts by reading and experimenting

Learn how text is split into tokens, why subword tokenizers exist, and how tokenization affects LLM behavior and cost.

Learn how neural networks learn patterns through layers, weights, and backpropagation.

Learn how RAG lets an LLM answer questions using relevant external documents fetched at query time.

Learn how embeddings turn text into vectors and enable semantic search by finding meaning-based similarity instead of keyword matches.

Learn what vector databases store, why nearest-neighbor search must be approximate at scale, and how ANN indexes (like HNSW and IVF) make retrieval fast.

Build a practical mental model for context limits and how to allocate tokens for better cost, speed, and answer quality.

Understand how token selection strategies control output quality, diversity, and consistency.

Learn how attention helps models decide what matters, from query-key-value math to multi-head behavior in modern transformers.

Understand how models call external code safely and reliably using structured outputs, validation, and execution boundaries.

Understand how Transformers use attention to process sequences in parallel and power modern LLMs.

Learn how to split documents into retrievable chunks, attach the right metadata, and index content so RAG retrieves the right context reliably.

Learn when to shape an LLM with prompts versus when to change its behavior with fine-tuning, and the trade-offs of each.

Learn why two-stage retrieval and keyword+vector fusion improve relevance in real-world RAG systems.