Generic model or fine-tuning? Evidence before investment
Limited proof with your domain's real data — accuracy, cost, and risk measured before scaling fine-tuning or proprietary RAG.
Leadership wants AI that understands business jargon, process, and sensitive data — but doesn't know if fine-tuning, RAG, or base model suffices. A structured evaluation tests hypotheses with real domain sample: accuracy by question type, hallucination rate, latency, and cost per transaction. You receive objective recommendation — proceed with fine-tuning, expand document base, or keep base model with guardrails — before committing MLOps and integration budget.
What blocks you today
Fine-tuning investment without evidence of gain over RAG or prompt engineering. Generic model hallucinates on sensitive data; team doesn't know when to scale to proprietary model. IT and leadership disagree on path — without comparable metric on real data.
What changes in practice
- Representative test case definition for domain and business risk
- Comparative proof: base model, RAG, and limited fine-tuning — with same metric
- Accuracy, hallucination, latency, and projected production cost report
- Architecture recommendation — fine-tuning, expanded RAG, guardrails, or hybrid
- Roadmap and go/no-go criteria for pilot or scale phase
Business outcome
Investment decision with evidence on real data — not generic benchmark slide. Fine-tuning enters only when proof shows measurable gain. IT and leadership align path, cost, and risk before big build.
Where it usually fits
- Companies with technical, regulatory, or operational jargon generic model gets wrong
- Cautious leadership wanting ROI before committing MLOps squad
- Operations with sensitive data where hallucination has high cost
- Projects that already tested generic chat and didn't reach minimum accuracy
- IT needing to justify fine-tuning versus expanding RAG or integration
How it evolves next
With evaluation complete, recommended path becomes measured pilot, production architecture, or integration plan — always with metric inherited from proof.
- Live pilot with recommended architecture and exception queue
- Production AI architecture with observability, rollback, and governance
- Internal assistant or copilot on expanded document base
- Integration plan connecting model to existing ERP, CRM, or channel
- AI usage policy, LGPD, and audit trail for go-live
Fine-tuning investment without evidence of gain over RAG or prompt engineering?
Generic model hallucinates on sensitive data? Let's talk — diagnosis and proof before the big investment.