LLM evaluation and fine-tuning
Know how good your AI system is before it ships — and after every change. We build held-out test sets, auditable LLM grading and model bake-offs on your own questions, and fine-tune when the data justifies it, not when a vendor suggests it.
"It seems better" is not a release criterion. The difference between an AI demo and a production system is a number you trust: accuracy on held-out questions, grading you can audit, regression checks on every change. We build that measurement layer — for systems we built and for systems somebody else built.
Fine-tuning is a tool, not a religion. We first check whether better retrieval, better prompts or a different model solves the problem; when fine-tuning is the right call — consistent tone, domain terminology, structured outputs, or agentic behaviour — we run it on data you control, evaluate it against the baseline, and document the trade-offs.
What is included
Held-out test sets
Question-and-answer sets from your domain that the system never saw — the benchmark that matters.
Auditable LLM grading
Model-graded quality with spot-checkable rubrics, so a number is always explainable.
Model bake-offs
Candidate models compared on your questions, with costs and latencies — a decision you can defend.
Regression gates
Every prompt, model or version change re-scored before release; quality stops drifting unnoticed.
Fine-tuning with a baseline
Agentic fine-tuning and domain adaptation, evaluated strictly against the untuned baseline.
How we work
- 1
Define quality
What a good answer means for your use case, turned into rubrics and test questions.
- 2
Measure the current system
Baseline scores on held-out sets, error analysis, and the highest-impact fixes identified.
- 3
Improve and gate
Fine-tuning or retrieval changes where they pay off, with regression gates so quality stays measured.
Frequently asked questions
When does fine-tuning make sense?
When behaviour must change, not just knowledge: consistent tone and format, domain terminology, structured outputs or multi-step tool use. When knowledge is the problem, retrieval (RAG) is usually the better answer. We measure both options against your test set before recommending one.
What is LLM evaluation, practically?
A held-out set of questions from your domain, a grading process (automated with auditable rubrics, human-verified where it counts), and scores reported per release. It turns "the AI feels better now" into a number and a diff.
How much data is needed for fine-tuning?
Less than most people expect for behaviour adaptation — hundreds of well-chosen examples can be enough. For domain knowledge, we usually recommend RAG instead. The evaluation tells you which path pays off on your data.