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Evaluate an AI assistant’s understanding of your business by testing it on realistic company tasks with approved reference material—not by relying on a general benchmark or a vendor’s claim. Define the intended work and risks, score correctness, completeness, source grounding, and uncertainty handling separately, then test the full system employees will use.
Define what “understands our business” means
There is no single score that establishes business understanding. Start with the work the assistant is meant to support and define observable outcomes for that work. NIST notes that “How a given component is measured and evaluated can change based on the context in which the AI system operates.” NIST’s AI measurement and evaluation guidance makes context central to evaluation.
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Before testing, document:
- Users and tasks: Which roles will use the assistant, and for what recurring work?
- Business goals: What should improve, such as the speed or consistency of a particular workflow?
- Authoritative sources: Which company records, policies, product documents, or other materials count as correct?
- Access boundaries: What data can each user and the assistant access, and what must remain restricted?
- Error consequences: What happens if the assistant is wrong, incomplete, or too confident?
- Requirements and risk tolerances: What must the assistant do reliably, and what failure rate or type of failure is unacceptable for the intended use?
The NIST AI RMF Core calls for organizations to define business-use context, goals, risk tolerances, and system requirements, and describes documented, repeatable evaluation and monitoring. Turn those ideas into explicit pass criteria before you look at results.
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Build a test set from real company work
Choose representative tasks from the roles and workflows in scope. For each one, prepare the reference material the assistant should use and a rubric that identifies required facts, acceptable alternatives, and claims it must not make. NIST’s draft AI 800-2 says evaluators should define objectives and choose benchmarks suited to them, including for a specific scenario or a deployment comparison. It is an initial public draft identified as January 2026, so check its publication status before treating it as current or normative.
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For example, a company-specific test set might ask the assistant to summarize a current account record from approved data, distinguish a customer request from company policy, explain a product limitation using current internal documentation, or say when the available material does not answer a question. These examples are test ideas, not findings about any particular assistant.
Include cases that reflect imperfect knowledge, not just clean questions with complete answers:
- Incomplete material, where a useful answer depends on asking for missing details.
- Outdated material, where the assistant should avoid presenting superseded information as current.
- Conflicting sources, where it should identify the conflict rather than quietly choose one.
- Insufficient evidence, where it should qualify its answer or abstain instead of inventing a resolution.
NIST-hosted work on evaluating machine-generated reports uses question-and-answer “nuggets” to assess completeness and accuracy, alongside citation checks for verifiability. Adapt that approach by listing the facts a good answer must cover and checking whether its supporting company sources actually substantiate them.
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The cited guidance does not establish a universal test-set size or pass threshold. Choose both according to task variety, risk, and intended use, and record why those choices are appropriate.
Score distinct dimensions separately
A single “good answer” rating can hide important weaknesses. Score each dimension against the task rubric and keep the results visible by task as well as in aggregate.
- Task correctness: Did the assistant give the right answer or take the right action for this company task?
- Completeness: Did it include the decision-relevant facts, constraints, and caveats the rubric requires?
- Grounding and traceability: Can important claims be traced to an authoritative company source, and does that source support what was said?
- Context handling: Did it keep teams, customers, products, policies, time periods, and permissions distinct rather than blending them?
- Uncertainty behavior: Did it seek clarification, qualify the answer, or abstain when evidence was missing or conflicting?
- Robustness in use: Did performance hold across representative users, different phrasings, realistic distractions, and changes to retrieved material or workflow?
These are practical dimensions synthesized from NIST’s context-sensitive measurement guidance, its work on report completeness and citation verifiability, and its agent-evaluation probes for faithfulness, completeness, and sufficiency. They are not a single official NIST rubric. NIST describes probes that compare generated claims with a human-curated corpus and leave a structured audit trail. That method can inform how you inspect answers and record evidence.
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Test the deployed experience, not just the model
Employees interact with a configured system, not an abstract model. Evaluate the assistant with the retrieval or connected knowledge sources, access controls, tools, and workflow it will actually use. Keep model-only results distinct from results for the complete application so a failure can be attributed more accurately.
Use ordinary cases alongside deliberately difficult or adversarial ones, and include field testing with representative users where possible. NIST’s ARIA program describes model testing, red-teaming, and field testing, and considers technical and contextual robustness as well as performance and accuracy.
For important answers, inspect the evidence behind them. Check whether a cited or retrieved source supports each material claim, whether relevant contrary evidence was missed, and whether the assistant exaggerated what a source says. A plausible answer is not necessarily a grounded one.
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Compare assistants on equivalent conditions
If you are evaluating more than one system, give each the same company-specific tasks, reference material, and operating conditions. Compare the same dimensions—correctness, completeness, grounding, uncertainty handling, robustness, and operational fit—and explain which matter most for the intended work and its risks.
Show component results and representative failures rather than letting one overall score decide the comparison. A high score on easy summaries, for example, cannot compensate for mishandling an important access boundary if that boundary is critical to deployment.
Interpret results and decide readiness
Report results by task and dimension. Record the assistant version and configuration, data snapshot, evaluation method, and relevant limitations, along with examples of failures. Judge readiness against criteria set before testing and calibrated to the consequences of errors.
General benchmark results are evidence about performance on that benchmark under its test conditions; they do not prove that an assistant understands a particular company. NIST’s February 2026 publication AI 800-3 notes that improvement on a benchmark does not always correspond to improvement on similar tasks outside it, distinguishing accuracy on a fixed benchmark from generalized accuracy. It does not provide a universal pass rate for business-context understanding.
Repeat the evaluation after material changes to the assistant, its knowledge sources, permissions, or workflow. A result applies to the configuration and conditions you tested, not automatically to a later version or different deployment.
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