Topic
Decoding Strategy
5 episodes
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Overview: Structured Output
We slow down and explain structured output from the ground up: why free-form model text is awkward for software, how schemas and constrained decoding make it usable, and where the format guarantee stops.
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Overview: Decoding Strategy
We finally slow down on decoding strategy, the rule that turns a model's next-token odds into the actual words you see. We use one hallway-and-doors picture to make greedy decoding, sampling, top-k, top-p, beam search, and newer decoding work feel less like magic knobs.
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Overview: Sampling and Temperature
We’re finally slowing down and explaining sampling and temperature from the ground up, because we keep circling back to it and it’s one of those knobs that sounds simple until it isn’t. We talk through why the same prompt can produce different answers, how temperature changes the shape of the next-token choices, and where the trade-offs actually land.
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Exploring Next Overview: Speculative Decoding
We finally slow down and unpack speculative decoding from the ground up: the draft model, the verify step, and why it can make generation faster without changing the output. We keep it concrete, because that trick sounds like cheating until the mechanism actually clicks.
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Overview: Autoregressive Generation
We finally slow down and make autoregressive generation click: the whole thing is just a model writing one token, then using what it wrote to choose the next one. We keep the focus on the loop, the trade-offs, and why that one-step-at-a-time setup is still the backbone of modern language models.