Session 1: Mental Models & Prompt Architecture
Token prediction mechanics, probability and temperature management, context-window optimization, and the 4-pillar architecture.
Key Learning Outcomes
How Language Models Actually Work
A language model does one thing: it predicts the next token given all previous tokens. Everything — reasoning, coding, translation — emerges from this single mechanism repeated thousands of times. The tokenizer converts your text into numbered tokens; common English words often map to a single token while rare words split into pieces. Understanding this explains why short, precise prompts outperform long, vague ones: fewer tokens, less noise, sharper probability distributions.
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