Fine-tuning

In recruiter context

ML Engineers in 2026 fine-tune for two reasons: (1) cost — a fine-tuned Llama 3.1 8B can match GPT-4o-mini on a specific task at 1/30th the per-token cost, which becomes meaningful at scale; (2) capability — for narrow specialised tasks where the prompted frontier model isn't passing your eval set. Fine-tuning makes sense when the task is narrow and stable, you have at least 1,000-10,000 high-quality examples, and the production volume justifies the engineering cost (usually tens of thousands of inferences per day minimum). Below that volume, prompting wins on operational simplicity.