Fragmented - AI Developer Podcast 303 - How LLMs Work - the 20 minute explainer
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Feb 2, 2026 A clear walkthrough of tokenization and how text becomes machine-friendly tokens. A tour of embeddings and analogies that show how meaning is encoded as vectors. A plain explanation of pretraining as the way models learn semantic maps. A practical look at inference, sampling methods, context windows, and cost tradeoffs.
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Three-Step Pipeline Behind LLMs
- LLMs convert human language into number-based representations so computers can do math with words.
- Tokenization, embeddings, and inference form the core pipeline that maps language to meaning.
Always Tokenize With The Model's Vocabulary
- Use a tokenizer to split text into the smallest meaningful units and map them to integer IDs.
- Remember each model has its own vocabulary lookup that defines token IDs for those units.
Why Tokens Become High-Dimensional Vectors
- Single integer token IDs lack semantic richness, so models use vectors to capture many traits per token.
- Embeddings map tokens to high-dimensional vectors where related words cluster together.
