Top-P Sampling

Top-P Sampling (nucleus sampling) is a generative AI technique that limits a model's word choices to the smallest set of probable tokens whose cumulative probability exceeds threshold P. For example, with top_p = 0.9, the model considers only the most likely tokens that together account for 90% of the probability mass. The technique was introduced by Holtzman et al. in 2019 as an alternative to fixed top-k sampling, and it adapts naturally to whether the model is confident (small candidate set) or uncertain (larger set). Most LLM APIs accept top_p alongside temperature, with typical values between 0.7 and 1.0. Combined with temperature, top-p shapes how diverse and creative outputs are. AI governance frameworks document sampling parameters in model cards as part of AI compliance and responsible AI deployment — particularly because changing top-p between development and production can subtly alter behavior in ways that affect AI risk management evaluations.

Centralpoint Documents Every Sampling Parameter: Oxcyon's Centralpoint AI Governance Platform records top-p settings alongside model identity and prompt — across ChatGPT, Gemini, Llama, and embedded models. Centralpoint meters every interaction, keeps prompts and skills on-prem, and embeds chatbots into any portal with one JavaScript line.


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