[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-fine-tune-vs-rag::en":3,"gloss-cluster-fine-tune-vs-rag::en":23,"gloss-next-fine-tune-vs-rag::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"fine-tune-vs-rag","core-ai","Fine-Tune vs RAG","Fine-tuning and retrieval solve different problems, and the choice is usually decided by whether your requirement is about behaviour or about facts.\n\nFine-tuning adjusts the model's weights on your examples. It is the right tool for形 form and style — a consistent output structure, a house tone, a classification scheme with fuzzy boundaries, a domain vocabulary the base model garbles. What it is bad at is knowledge that changes: retrain every time a price list moves and you have built a very expensive database.\n\nRetrieval leaves the model alone and puts the relevant text in front of it at request time. It handles freshness, per-customer data and citation — the answer can point at the document it came from, which fine-tuning cannot do.\n\nMost teams asking for a fine-tune describe a retrieval problem: \"it doesn't know our products\" is a facts issue. The order that works is prompt, then retrieval, then fine-tune only when the failure that remains is stylistic or structural rather than factual — and by then you have an evaluation set from the first two stages to prove the third was worth it.","Fine-tuning teaches a model how to behave; retrieval tells it what is true right now. Most products that think they need the first need the second.",null,[11,14,17,20],{"slug":12,"name":13},"cost-per-task","Cost per Task",{"slug":15,"name":16},"fine-tuning","Fine-Tuning",{"slug":18,"name":19},"prompt-engineering","Prompt Engineering",{"slug":21,"name":22},"retrieval-augmented-generation","Retrieval-Augmented Generation (RAG)",[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"]