Fine-Tuning Feasibility —Is Fine-Tuning Right for Your Use Case?

I will assess your use case and deliver a written fine-tuning feasibility report covering whether fine-tuning is the right approach versus prompt engineering or RAG, your data requirements, estimated training cost, infrastructure needs, and a realistic quality improvement estimate before you commit training budget. Fine-tuning is frequently recommended when prompt engineering or RAG would solve the problem faster, cheaper, and without the maintenance overhead of a custom model — the decision requires honest assessment of whether your task genuinely requires model weight adjustment or whether your prompts and retrieval architecture are simply under-engineered.

The report covers your use case analysis, prompt engineering and RAG alternatives assessment, data availability and quality review, training cost estimate, infrastructure requirement, expected quality improvement, and a go/no-go recommendation with rationale.