[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-knowledge-cutoff::en":3,"gloss-cluster-knowledge-cutoff::en":26,"gloss-next-knowledge-cutoff::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"knowledge-cutoff","core-ai","Knowledge Cutoff","A knowledge cutoff is the date after which a model has no training data — it simply doesn't know about events, product releases, API changes, or prices that happened later. Ask a model with an early-2024 cutoff about a framework released in 2025 and it will either say it doesn't know or, worse, confidently invent an answer. The cutoff is a property of the pretraining data, not the model's release date, and the two can differ by months. For SaaS builders this matters constantly: any feature that touches current facts — news, live prices, your own changing docs, today's inventory — cannot rely on the model's parametric memory. The fix is retrieval-augmented generation or tool\u002Fweb-search calls that inject fresh, authoritative data into the prompt at request time. Always tell users when an answer may be stale, and never hard-code assumptions about what the model \"knows\" — cutoffs move forward with every model generation, so a limitation you designed around last year may no longer apply.","A knowledge cutoff is the date a model's training data ends — after it the model knows nothing about new releases, APIs, or prices, and may invent them.",null,[11,14,17,20,23],{"slug":12,"name":13},"function-calling","Function Calling (Tool Use)",{"slug":15,"name":16},"grounding","Grounding",{"slug":18,"name":19},"hallucination","Hallucination",{"slug":21,"name":22},"llm","Large Language Model (LLM)",{"slug":24,"name":25},"retrieval-augmented-generation","Retrieval-Augmented Generation (RAG)",[27,31,35,39,42,45,48,51,54,57,60,63],{"slug":28,"category":5,"name":29,"updated_at":30},"agentic","Agentic AI","2026-08-24T02:46:36+00:00",{"slug":32,"category":5,"name":33,"updated_at":34},"alignment-tax","Alignment Tax","2026-08-24T02:46:37+00:00",{"slug":36,"category":5,"name":37,"updated_at":38},"artificial-intelligence","Artificial Intelligence (AI)","2026-08-24T02:46:38+00:00",{"slug":40,"category":5,"name":41,"updated_at":30},"attention","Attention",{"slug":43,"category":5,"name":44,"updated_at":38},"beam-search","Beam Search",{"slug":46,"category":5,"name":47,"updated_at":34},"benchmark-contamination","Benchmark Contamination",{"slug":49,"category":5,"name":50,"updated_at":34},"catastrophic-forgetting","Catastrophic Forgetting",{"slug":52,"category":5,"name":53,"updated_at":38},"computer-vision","Computer Vision",{"slug":55,"category":5,"name":56,"updated_at":34},"constitutional-ai","Constitutional AI",{"slug":58,"category":5,"name":59,"updated_at":30},"context-window","Context Window",{"slug":61,"category":5,"name":62,"updated_at":38},"deep-learning","Deep Learning",{"slug":64,"category":5,"name":65,"updated_at":30},"diffusion-model","Diffusion Model"]