To find out whether an AI assistant understands your company’s business context, test it on representative company tasks using approved reference material—not on general AI benchmarks alone. Score task correctness, completeness, source grounding, context handling, uncertainty behavior, and reliability in the workflow people will actually use. Record the system and data configuration, review failures, and repeat the evaluation when anything material changes.
Define what “understands our business” needs to mean
There is no single score that proves an assistant understands a company. The useful question is whether it can perform specific work for specific users, using permitted sources, within an acceptable level of risk. NIST puts the principle plainly: “How a given component is measured and evaluated can change based on the context in which the AI system operates.” (NIST, AI measurement and evaluation.)
Before testing, document:
- Users and tasks: Who will use the assistant, and what work should it help them complete?
- Business goal: What outcome is it intended to support?
- Approved sources: Which records, policies, product documents, or other materials count as authoritative?
- Access boundaries: What information may each user or role see?
- Error consequences: What could happen if the assistant is wrong, incomplete, or overconfident?
- Requirements and tolerances: What must a suitable answer contain, and what kinds of failure are unacceptable?
NIST’s AI Risk Management Framework (AI RMF) Core calls for defining business value and use context, organizational goals, risk tolerances, and system requirements, as well as documenting repeatable evaluation and monitoring. NIST AI RMF Playbook.
Turn those points into observable requirements. For example, an assistant might need to summarize a current account record from approved data, distinguish company policy from a customer’s request, explain a product limitation from current documentation, or say when available information does not answer the question. These are test ideas to adapt to your business, not a standard test or a reported NIST result.
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Choose tasks that reflect the roles and workflows in scope. For each task, prepare the reference material and a rubric or answer notes. Specify the required facts, acceptable alternatives, and claims the assistant must not make. Include straightforward cases as well as questions involving missing, stale, or conflicting information.
Some cases should have a correct outcome of asking a clarifying question or stating that the evidence is insufficient. Otherwise, the test may reward confident completion even when the company context does not support an answer.
Choose evaluation methods to fit the purpose. NIST’s initial public draft AI 800-2, identified as January 2026, says evaluators should define objectives and select benchmarks suited to them, including evaluating fitness for a particular scenario or comparing systems for deployment. Check the publication’s status before treating that draft as current or normative. NIST AI 800-2, initial public draft.
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A useful way to make each case checkable is to list the essential facts as questions and expected answers, then verify both completeness and source support. NIST’s framework for evaluating machine-generated reports uses “nuggets” in this question-and-answer form and includes citation checks for verifiability. NIST machine-generated-report evaluation framework.
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No universal test-set size or pass threshold is established by these sources. Select the number and variety of cases, and the acceptance criteria, according to task diversity, intended use, and the consequences of failure. Write down those choices before evaluating so the bar is not adjusted after seeing results.
Measure separate dimensions, not just an overall score
Score each dimension separately. A high score on one cannot compensate for a critical weakness in another.
- Task correctness: Did the assistant produce the right answer or action for the company task?
- Completeness: Did it include the decision-relevant facts, constraints, and caveats required by the rubric?
- Grounding and traceability: Can important claims be traced to an authoritative company source, and does that source actually support them?
- Context handling: Did it distinguish relevant teams, customers, products, policies, time periods, and access permissions instead of blending them together?
- Uncertainty behavior: Did it ask for missing details, qualify an answer, or abstain when evidence was insufficient or conflicting?
- Robustness in use: Did it perform consistently across representative users, different wording, realistic distractions, and changes to retrieved material or workflow?
These dimensions synthesize NIST’s context-sensitive measurement guidance, its report-completeness and citation-verifiability approach, and agent-probe work on faithfulness, completeness, and sufficiency. They are a practical evaluation framework, not a single pre-existing NIST rubric. NIST measurement and evaluation; NIST report-evaluation framework; NIST agentic evaluation probe work.
When comparing assistants, give them the same company tasks, reference material, and operating conditions. Show results by dimension and include representative failures. A single aggregate score can hide a serious weakness in grounding or uncertainty handling, for example. The cited sources do not establish a universal numeric cutoff or support a vendor ranking.
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Test the complete experience people will use
A model-only result does not establish how a deployed assistant will perform. Evaluate the configured experience, including its connected knowledge sources or retrieval, access controls, tools, and relevant workflow. Keep model-only tests distinct from tests of the complete application so you can identify where a failure occurs.
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Use a mix of ordinary task cases, deliberately difficult or adversarial cases, and field testing with representative users when possible. NIST’s ARIA program describes model testing, red-teaming, and field testing, and considers technical and contextual robustness alongside performance and accuracy. NIST ARIA.
For important answers, inspect the evidence, not just whether the final response sounds plausible:
- Does the cited or retrieved source support the claim as stated?
- Was relevant contrary evidence overlooked?
- Did the assistant overstate what the source establishes?
- Was the information available to that user under the intended permissions?
NIST’s agentic evaluation probe work describes checking generated claims against a human-curated corpus and assessing faithfulness, completeness, and sufficiency with a structured audit trail. NIST agentic evaluation probe work.
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Interpret results and decide what to do next
Report performance by task and dimension, with examples of meaningful failures. Record the assistant version and configuration, data snapshot, evaluation method, and limitations. Compare results with criteria set before testing and calibrated to the consequences of failure. A result that is acceptable for a low-risk drafting task may not be acceptable for a task where an incorrect answer has serious consequences.
Repeat the evaluation after material changes to the assistant, its knowledge sources, permissions, or workflow. A prior result describes the configuration and conditions that were tested; it does not automatically apply to a changed system.
General benchmark scores are limited evidence, not proof of company-specific understanding. NIST AI 800-3, published in February 2026, notes that benchmark improvement does not always correspond to improvement on similar tasks outside the benchmark and distinguishes accuracy on a fixed benchmark from generalized accuracy. It does not set a universal pass rate for business context. NIST AI 800-3.
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