Controlling How LLM’s Respond

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This image summarizes the key controls (hyperparameters) used to shape how a large language model (LLM) generates text. At a high level, these settings influence creativity, focus, length, and repetition of responses.

• Temperature – Controls randomness. Higher values make responses more creative and diverse; lower values make them more predictable and focused.
• Top-K – Limits the model to choosing from the top K most likely next words, reducing unlikely or noisy outputs.
• Top-P (nucleus sampling) – Selects from the smallest set of words whose total probability exceeds a threshold, balancing diversity and coherence.
• Max Response (tokens) – Sets the maximum length of the generated output.
• Frequency Penalty – Reduces repetition by penalizing words that have already been used frequently.
• Presence Penalty – Encourages introducing new topics or words rather than repeating existing ones.

Together, these controls act like “communication style knobs,” adjusting how the model speaks—its tone, variety, and verbosity.

We’re going to cover each item in the next sections.