Should You Fine-Tune? The 2026 Decision Ladder (Prompt RAG LoRA Distill)
Originally published on AI Tech Connect. The one question that saves you a GPU bill Somewhere in the lifecycle of almost every AI feature, a team asks: "should we fine-tune?" The honest answer, the overwhelming majority
Originally published on AI Tech Connect.
The one question that saves you a GPU bill Somewhere in the lifecycle of almost every AI feature, a team asks: "should we fine-tune?" The honest answer, the overwhelming majority of the time, is "not yet". It is a question that arrives too early, usually because fine-tuning sounds like the serious, grown-up move β the thing real machine-learning teams do, the lever that turns a generic model into your model. So a Bengaluru fintech building a transaction-narration feature, or a Manchester health-tech drafting clinical letters, spins up a training pipeline before it has wrung the value out of the cheaper rungs below. The cost of asking too early is concrete. Fine-tuning is not free even when the GPU is cheap: you take on a data-curation effort, a training and evaluation loop, a model youβ¦
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