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Build an LLM with Claude in 3 Hours
Preparing your Interaction Arc…
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An engineer may need implementation mastery. An architect may need boundaries, tradeoffs, and system behavior. A business or product leader may primarily need capabilities, constraints, and decision points.
Same LLM. Same DigiRef. Different traversal.
You direct. Claude builds. You choose how deep to go.
Strip away the branding and a large language model is a function with a boring signature:
f(sequence of tokens) -> probability distribution over the next tokenThat is it. Everything else - chat, code generation, agents, tool use, the whole industry - is built on top of that one operation, applied repeatedly.
This is worth sitting with, because most confusion about LLMs comes from imagining a richer core than actually exists. The model does not "look things up." It does not "decide what to say." It emits a probability distribution over its vocabulary, something samples from that distribution, the chosen token is appended to the sequence, and the whole function runs again. A paragraph is that loop run a few hundred times.
Two consequences follow immediately, and they will matter in Parts X and XIII:
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