llm-systems-foundations
24 Steps • ~90m runtimeLLM Systems Foundations
Build an end-to-end foundation across tokenization, transformers, inference latency, prompt engineering, structured outputs, semantic caching, and safety boundaries.
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Full Concept Guidefoundation-models-multimodal
lesson
Tokens and Tokenization
How model inputs become discrete identifiers and why token boundaries affect cost and meaning.
Architectural Intuition
A language model does not read characters or words directly. A tokenizer converts text into a sequence of IDs drawn from a fixed vocabulary; the model operates on the learned vectors associated with those IDs.
<ConceptDiagram sourceIds="transformer-paper|hf-tokenizers" steps="Text|Tokenizer rules|Token IDs|Embedding lookup" />
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Foundational Prerequisites (0)
First-principles foundation node.
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Embeddings
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Context Windows
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Micro-Assessment Verification
Which statement best captures the operating model for Tokens and Tokenization?