Supervised fine-tuning learns exactly what your experts demonstrate.

What SFT and instruction tuning are

Supervised fine-tuning (SFT) teaches a model by example: you give it prompts paired with the responses you want, and it learns to imitate them. Instruction tuning is SFT on instruction-and-response pairs, so the model learns to follow directions across tasks. Unlike RLHF, which learns from preferences between outputs, SFT learns directly from demonstrations — which means the model inherits the exact quality, and the exact errors, of the people who wrote them.

That makes the author the whole story. A demonstration written by someone who doesn't truly understand the domain doesn't just fail to help — it teaches the model to reproduce a confident mistake. For frontier and specialist capabilities, the demonstration has to come from a practitioner: the clinician who writes the differential the way it should be reasoned, the litigator who structures the argument correctly, the engineer whose worked solution is actually right.

Why the demonstration has to come from an expert

Why the demonstration has to come from an expert. DeepenSkill sources practising specialists through an aggregated network of specialist recruiters, BPOs and individual practitioners — not a single pool — and holds every one of them to the same bar: credential verification, a standardized domain-knowledge test, and a practical task against your live spec. The people writing your SFT data are qualified to be the model's teacher.

What we deliver

What we deliver. High-quality prompt-response demonstrations for supervised fine-tuning. Instruction-following data across your target tasks. Long-form, step-by-step reasoning traces where the how matters as much as the answer. Domain rubrics and written guidelines so demonstrations are consistent across contributors. And correction or rewriting of an existing SFT set an expert review reveals to be weak.

Proof on every demonstration

Proof on every demonstration. Because SFT data is imitated directly, silent quality problems are expensive — you discover them only after the model has learned them. DeepenSkill measures before you do. A validation team separate from the delivery team scores every item against gold standards; each contributor carries a live quality rating; every demonstration traces to a verified author, a task spec and a timestamp. You receive an evidence pack you can verify yourself, not a batch-average assurance.

Domains

Domains. Medicine and life sciences, law and regulatory, physics and mathematics, software and security, advanced engineering, finance and actuarial.

DeepenSkill is Deepen AI's expert-workforce platform — an eight-year data-infrastructure company whose customers audit their suppliers (BMW, Aptiv, Bosch, Cadence and Daimler Trucks) and a co-author of ASAM OpenLABEL. You sign one contract and get experts producing to your spec: never a roster to manage, never a supply chain to audit.

FAQ

• What is supervised fine-tuning (SFT)?

  A training method where a model learns from prompt-response examples — demonstrations of the behavior you want — by imitating them.

• What's the difference between SFT and RLHF?

  SFT learns directly from demonstrations (do it like this); RLHF learns from preferences between outputs (this one is better than that one). Most frontier pipelines use SFT first, then RLHF.

• Why does DeepenSkill use domain experts for SFT?

  Because the model imitates the demonstration exactly — a non-expert example teaches a confident error. Expert-authored demonstrations are the whole point at the frontier.

• What SFT and instruction-tuning work do your experts do?

  Prompt-response demonstrations, instruction-following data, long-form reasoning traces, domain rubrics, and expert correction of existing SFT sets — all independently validated.

• How do you validate SFT quality?

  Independent scoring against gold standards, per-contributor quality ratings, and full provenance on every demonstration, delivered as verifiable evidence.