SAMPLING AND STRUCTURED OUTPUTS IN LLMS

Summary from the TLDR AI newsletter

Sampling is the process of selecting a token from a model's vocabulary based on the probability distribution. Structured outputs allow models to turn unstructured data into structured data. Together, they both determine a model's next chosen token. Large language models are extremely sensitive to the slightest variation in their system prompts, templating, structured outputs, and sampling parameters.