[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-top-k::en":3,"gloss-cluster-top-k::en":23,"gloss-next-top-k::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"top-k","core-ai","Top-k Sampling","Top-k is a sampling parameter that restricts a language model to choosing its next token only from the k candidates with the highest predicted probability, re-normalizing over that shortlist and sampling from it. Everything outside the top k is discarded outright, no matter how the probability mass happens to be distributed. It is the simpler cousin of top-p (nucleus sampling): where top-p keeps however many tokens are needed to reach a cumulative probability threshold, top-k keeps a fixed count and ignores the shape of the distribution entirely. That difference is the whole argument between them. When the model is confident and one token carries most of the probability, a fixed k of 50 drags in 49 candidates that should never have been considered; when the model is genuinely uncertain across many plausible continuations, the same k may cut off options that deserved a chance. This is why top-p is generally regarded as the more adaptive of the two — it responds to how peaked or flat the distribution is at each step — though neither is superior in an absolute sense, and they solve the same problem by different means. Top-k has no direct effect on cost: it changes which token gets selected, not how many tokens are processed or billed. Its effect is on character. A very low k narrows output diversity and makes generated text feel repetitive and mechanical, which is occasionally what you want; a very high k approaches unconstrained sampling and reintroduces the long tail of low-probability tokens that truncation exists to remove. For SaaS builders the practical guidance is to change one sampling knob at a time and to leave top-k at its provider default unless you have a specific reason — setting top-k, top-p, and a high temperature together is a common way to produce degenerate output while believing you are tuning creativity. Many APIs expose top-k on some model families and not others, so treat it as a portability hazard in any prompt configuration you plan to move between vendors. It is also worth knowing that top-k and top-p are frequently active at the same time, applied in sequence: the shortlist is truncated by count and by cumulative probability, and whichever constraint bites first wins. That composition is harmless when both are near their defaults and quietly destructive when both are set aggressively, because each one is individually reasonable while the intersection can leave the model almost no room to choose. The failure mode looks like a model that has become oddly rigid or has started looping, and it is easy to misdiagnose as a model-quality problem rather than a configuration one. And remember what truncation cannot do: none of these parameters make a model more accurate. They only shape which of the model's own candidate continuations are eligible, so a factual error that sits at the top of the distribution will be selected under any k you choose. Reducing hallucination is a grounding and retrieval problem, not a sampling one.","Top-k sampling limits an LLM's next-token choice to the k highest-probability candidates — a fixed-size cousin of top-p's probability-mass cutoff.",null,[11,14,17,20],{"slug":12,"name":13},"hallucination","Hallucination",{"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"]