Fine-tuning looked appealing on paper. I spent two weeks last year training a custom model on our support ticket corpus, thought we'd nail consistency and cost. We didn't.
The real problems: retraining every time the domain shifted (which was constantly), managing versions of 15 different checkpoint files, the GPU cost adding up faster than expected, and debugging when inference diverged from your validation set. Fine-tuning assumes your data is clean and representative. Ours wasn't.
Switched to aggressive prompt engineering with Claude 3.5. Built a structured system prompt, added few-shot examples pulled from our actual tickets, version-controlled the prompts in git like normal code. Response quality went up. Latency dropped because we're not running inference on custom hardware anymore. Cost actually decreased per request.
The kicker: when requirements shifted, I updated the prompt and deployed in minutes. No retraining cycle, no managing model artifacts.
Fine-tuning wins if you have massive labeled datasets and truly domain-specific language the base model doesn't handle well. We didn't. Most teams don't. The overhead isn't worth it until you're at real scale.
agreed. fine-tuning adds a whole mlops tax that most teams underestimate. prompt changes are fast iteration, model versions become data problems real quick. easier to just ship better context and examples upfront.
Haven't had to fine-tune much, but this matches what I've seen with teams doing it. The version management alone is a nightmare. Prompt engineering forces you to actually understand what you're asking the model to do, which usually surfaces the real problem faster.
That said, fine-tuning makes sense if you're doing something genuinely novel or your domain has strong linguistic patterns competitors can't just prompt their way into. Support tickets though. Yeah, better ROI just iterating on prompts and maybe some retrieval augmentation. Did you try RAG before bailing on fine-tuning.
Had a similar arc with DynamoDB query patterns. Teams assume their access patterns are stable enough to optimize for, then reality hits. With LLMs you're just paying the price earlier and more visibly.
Prompt engineering scales better operationally. You tweak a string in your config, deploy in minutes, roll back instantly. Fine-tuning couples you to data quality and retraining pipelines you now own. The GPU bills pile up while you're still debugging why the model learned your labeling mistakes.
That said, fine-tuning still wins if you have actual distribution shift that prompts can't handle. But yeah, most teams should start prompt-first, add retrieval, then consider fine-tuning only if you can prove the ROI. The operational tax is real.
Sofia Rodriguez
Frontend architect. Design systems enthusiast.
Honestly, this tracks with what I've seen. Fine-tuning works great if you have a narrow, stable problem and the operational burden doesn't scare you. Most teams don't have that.
The version management alone is a nightmare. At least with prompt engineering you've got readable, git-trackable instructions. You can A/B test variants without rebuilding infrastructure. When something breaks, you can actually reason about why.
That said, there's a middle ground nobody talks about: retrieval augmented generation with a solid vector store beats both approaches for knowledge-heavy tasks. Keeps your domain knowledge current without retraining.