[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-overfitting::en":3,"gloss-cluster-overfitting::en":26,"gloss-next-overfitting::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"overfitting","core-ai","Overfitting","Overfitting is when a model learns the training data too literally — memorizing its noise and quirks instead of the general pattern — so it scores well in training but fails on new, unseen inputs. It's a core machine-learning failure mode and the reason you always evaluate on data the model never saw. For SaaS builders, overfitting is the practical risk whenever you fine-tune or train a small custom model: feed it a few hundred examples for too many epochs and it will parrot them back beautifully while generalizing poorly to real user inputs. Signs include a large gap between training and validation performance and a model that's brittle to small rephrasings. Defenses are standard: hold out a validation and test set, stop training when validation stops improving (early stopping), use more and more varied data, and prefer lightweight adaptation (LoRA, prompting, retrieval) over heavy fine-tuning. The same idea shows up in evals — a model or prompt \"overfit\" to your benchmark can look great on it and disappoint in production.","Overfitting is a model memorizing the noise in its training data instead of the pattern — high training scores, poor performance on anything new.",null,[11,14,17,20,23],{"slug":12,"name":13},"catastrophic-forgetting","Catastrophic Forgetting",{"slug":15,"name":16},"fine-tuning","Fine-Tuning",{"slug":18,"name":19},"llm-benchmark","LLM Benchmark",{"slug":21,"name":22},"lora","Low-Rank Adaptation (LoRA)",{"slug":24,"name":25},"synthetic-data","Synthetic Data",[27,31,35,39,42,45,48,49,52,55,58,61],{"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":12,"category":5,"name":13,"updated_at":34},{"slug":50,"category":5,"name":51,"updated_at":38},"computer-vision","Computer Vision",{"slug":53,"category":5,"name":54,"updated_at":34},"constitutional-ai","Constitutional AI",{"slug":56,"category":5,"name":57,"updated_at":30},"context-window","Context Window",{"slug":59,"category":5,"name":60,"updated_at":38},"deep-learning","Deep Learning",{"slug":62,"category":5,"name":63,"updated_at":30},"diffusion-model","Diffusion Model"]