Specialized domains need custom evaluation datasets

Off-the-shelf benchmarks rarely measure whether a model will work in a specialized enterprise domain. A model can score well on public tests and still fail on proprietary terminology, internal workflows, rare customer intents, regulatory constraints, or domain-specific document structures. When no benchmark exists, the team has to build one.

NVIDIA NeMo Data Designer generates and validates candidate eval data. The workflow should start with trusted seeds: real examples, approved documents, task taxonomies, product docs, policies, code snippets, support cases, or expert-authored scenarios loaded from local files. Then teams generate targeted variations, label expected behavior, add hard cases, validate structure, score open-ended outputs with judge rubrics, and route high-risk examples to human review. For domain and multilingual grounding, NVIDIA's published Nemotron supervised fine-tuning datasets show the kind of specialized open data teams can seed from.

Teams should distinguish training data from evaluation data. Eval datasets should include a stable set of prompts, inputs, expected outputs or scoring criteria, unanswerable cases, hard negatives, and failure modes. For RAG-heavy domains, include query-document pairs, relevant passages, hard negatives, source freshness, citation faithfulness, and abstention behavior. For agents, include tool choice, arguments, permissions, tool errors, handoffs, escalation, recovery, and final-answer grounding.

Key takeaways

• Specialized domains often need custom eval sets because public benchmarks miss the workflow.
• Eval datasets should be stable, reviewable, and tied to task-specific scoring criteria.
• NVIDIA NeMo Data Designer generates and validates candidate eval examples from seeds.
• RAG and agent evals need step-level and evidence-level checks, not just final-answer scoring.