[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-logprobs::en":3,"gloss-cluster-logprobs::en":23,"gloss-next-logprobs::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"logprobs","core-ai","Logprobs (Log Probabilities)","Logprobs are the log-probabilities a model assigns to each token it considers when generating text — essentially, how likely the model thought each word was. Many LLM APIs can return them alongside the output, and they're a practical window into the model's internal confidence that the visible text alone doesn't give you. Builders use logprobs for real jobs: estimating how sure the model is about an answer, building classifiers by comparing the probabilities of candidate labels rather than parsing free text, and flagging low-confidence generations for review or a fallback. Because they reflect the model's own probability estimates, they're often a better confidence signal than asking the model \"how sure are you?\" Practical note: availability and format vary by provider, so check whether your model exposes logprobs before designing around them. And remember token probabilities measure the model's confidence, not truth — a model can be highly confident and wrong, so use logprobs as one input to a decision, not a guarantee of correctness.","Logprobs are the log-probabilities a model assigned to each token it considered — a practical window into confidence that the visible text alone can't give.",null,[11,14,17,20],{"slug":12,"name":13},"model-calibration","Model Calibration",{"slug":15,"name":16},"temperature","Temperature",{"slug":18,"name":19},"token","Token",{"slug":21,"name":22},"top-p","Top-p (Nucleus Sampling)",[24,28,32,36,39,42,45,48,51,54,57,60],{"slug":25,"category":5,"name":26,"updated_at":27},"agentic","Agentic AI","2026-08-24T02:46:36+00:00",{"slug":29,"category":5,"name":30,"updated_at":31},"alignment-tax","Alignment Tax","2026-08-24T02:46:37+00:00",{"slug":33,"category":5,"name":34,"updated_at":35},"artificial-intelligence","Artificial Intelligence (AI)","2026-08-24T02:46:38+00:00",{"slug":37,"category":5,"name":38,"updated_at":27},"attention","Attention",{"slug":40,"category":5,"name":41,"updated_at":35},"beam-search","Beam Search",{"slug":43,"category":5,"name":44,"updated_at":31},"benchmark-contamination","Benchmark Contamination",{"slug":46,"category":5,"name":47,"updated_at":31},"catastrophic-forgetting","Catastrophic Forgetting",{"slug":49,"category":5,"name":50,"updated_at":35},"computer-vision","Computer Vision",{"slug":52,"category":5,"name":53,"updated_at":31},"constitutional-ai","Constitutional AI",{"slug":55,"category":5,"name":56,"updated_at":27},"context-window","Context Window",{"slug":58,"category":5,"name":59,"updated_at":35},"deep-learning","Deep Learning",{"slug":61,"category":5,"name":62,"updated_at":27},"diffusion-model","Diffusion Model"]