LLM Fine-Tuning Data
Instruction and preference datasets written and reviewed by people who understand the task — the part of fine-tuning that actually determines the result.
What is included
LLM Fine-Tuning Data, in detail
Instruction datasets
Prompt and response pairs written to your domain, tone and difficulty distribution.
Preference data
Ranked response pairs with written rationales, for preference-based training.
Domain expert review
Specialist reviewers where the subject matter genuinely requires them.
Evaluation sets
Held-out sets built to test the behaviours you actually care about.
Red-team prompts
Adversarial cases written to probe failure modes before users find them.
How we work
Four steps, no surprises
- ConsultationWe learn the business and what success looks like.
- Audit & scopeA written plan: what we build, in what order, at what cost.
- BuildDelivered in stages you review as we go.
- SupportMonitoring and iteration once it is live.
Questions
Frequently asked
How many examples do we need?
For narrow task tuning, often a few thousand well-written examples beats tens of thousands of scraped ones. We scope from your task.
Do you use models to generate the data?
Only where it is appropriate, and always with human review. We tell you which parts were model-assisted.
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