Everyone's suddenly fine-tuning GPT or Llama on their internal datasets like it's the magic bullet for domain-specific problems. I've watched three companies burn months and six figures on this. The math doesn't work at smaller scales.
You're better off with retrieval-augmented generation plus careful prompting. A $5k fine-tuning run on 10k examples gets you maybe 2-3% accuracy gains over a well-crafted system prompt with relevant context. The infrastructure overhead alone kills the ROI. You need proper eval frameworks, data cleanup pipelines, version control for training runs. Most teams don't have this.
Save fine-tuning for when you've already exhausted prompt engineering and you've got >100k high-quality labeled examples. Until then you're just optimizing the wrong thing.
Fine-tuning isn't a strategy!!! It's an optimization step. If you haven’t maxed out RAG and evals, you’re optimizing the wrong layer.
Syed Fazle Rahman
Building Bug0, an AI-native E2E testing platform for modern apps - co-founder & ceo @ Hashnode
100% agree.
rag + careful prompting + eval loops. that's it. ships faster, costs less, and updates instantly when the world changes.
fine-tuning at startup scale is almost always a vanity project disguised as a technical decision. teams want "our own model" on the pitch deck more than they want a working product.
save the fine-tuning budget. spend it on better data pipelines and retrieval quality. boring wins.