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Compare LLM decoding strategies: greedy, beam search, temperature, top-k, top-p, and repetition penalties.

How you decode from the model's probabilities shapes the output as much as the model. The signal is knowing the deterministic vs sampling methods and when each fits (factual vs creative). Here is the answer.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

How you decode from the model's probabilities shapes the output as much as the model. The signal is knowing the deterministic vs sampling methods and when each fits (factual vs creative). Here is the answer.

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LEARN THE BACKGROUND

These lessons teach the material this question tests, in order and from the beginning.

Applied AI Engineering·How language models work, enough to build onFree12mWhy the same prompt gives a different answer twiceRandomness in a model is a setting, not a mystery, and knowing which knob does what is the difference between a feature that behaves and one that surprises you in production. This lesson also covers why turning it all the way down still does not give you exact repeatability.
UP NEXT ON YOUR JOURNEY
Next in this trackWhat's the difference between masked language modeling (BERT) and causal language modeling (GPT)?Next in this trackWhat is QLoRA, and how does it make fine-tuning large models feasible on one GPU?Next in this trackHow does constrained / structured decoding force an LLM to emit valid JSON or grammar?
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