☕ Learning path

Learn AI Infrastructure.

The plumbing behind every AI feature: how text becomes tokens and vectors, and how models are grounded in real data.

⏱ ~35 min· 4 concepts· 3 hands-on tools· free, no account
0 of 7 steps
  1. ReadWhat is a context window? The model's working memory, tokens, what fills it, why it runs out, and how to manage it.
  2. Trytoken counter Count tokens with the real OpenAI tokenizers.
  3. ReadWhat are embeddings? How content becomes a vector that captures meaning, the engine behind semantic search, RAG, and recommendations.
  4. Tryembedding explorer Real embeddings + semantic search, in-browser.
  5. ReadWhat is a vector database? Where embeddings live, fast nearest-neighbour search over millions of vectors, and the backbone of RAG.
  6. ReadWhat is RAG (Retrieval-Augmented Generation)? Grounding a model in your own documents at query time, embeddings, retrieval, and why it beats fine-tuning for facts.
  7. Tryrag chunking visualizer See how a document splits into RAG chunks.

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