Every time you prompt a large language model, you're witnessing the result of thousands of sequential sampling decisions—each token drawn from a probability distribution over the model's vocabulary. In standard chat interfaces, these distributions are sampled with fixed settings that users cannot adjust. But API
Log probabilities (logprobs) provide a window into how confident a language model is about its predictions. In this technical implementation, we demonstrate how to access and interpret logprobs via the OpenAI API, using a series of increasingly difficult multiplication tasks. Our experiment reveals that declining confidence scores can effectively signal
Large language models (LLMs) rely on a set of parameters that directly influence how text is generated — affecting randomness, repetition, length, and coherence. Understanding of these parameters is essential when working with LLMs in research, application development, or evaluation settings. While chat-based interfaces such as ChatGPT, Copilot, or Gemini typically
