Decoding Strategies and Output Control

# How AI Decides What to Say Next When you type a question into an AI chatbot, the AI doesn't just pick the next word randomly—it uses specific strategies to choose responses that are useful, creative, or accurate depending on what you need. These strategies control things like how repetitive the AI gets, how risky its answers are, and whether it sticks to a particular format (like always giving you a numbered list). Think of it like adjusting the settings on a GPS: you can ask for the fastest route, the scenic route, or one that avoids highways, and the AI's "decoding strategies" work the same way to shape how it responds to you.
This chapter is divided into nine parts; they are: • Reading Logits from a Model • Greedy Decoding • Temperature Sampling • Top-$k$ Sampling • Nucleus Sampling • Repetition Penalties • Beam Search • Stop Conditions • Structured Output Constraints The model returns a vector of logits for every positi
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