Testing AI-based features, such as automated summary generation in CRM systems, requires moving beyond static expected results. Since generative models can produce varied outputs, developers must establish a behavioral contract that defines mandatory properties for acceptable responses.
A test oracle approach involves combining programmatic assertions with qualitative criteria. Key metrics include factuality, ensuring all claims are supported by the source dialogue, and completeness, verifying that essential information like order IDs and customer requests is preserved without hallucinating actions like approved refunds.
To evaluate performance, teams should use a multi-dimensional assessment strategy, such as the HELM framework. This involves creating test cases with specific constraints, such as character limits and forbidden claims, and running multiple iterations to calculate a pass rate rather than relying on a single output.
Automated evaluation using LLMs as a judge requires human-labeled datasets to calibrate the system. Discrepancies between human and model judgments should trigger a review of the rubric. It is critical to track failure categories separately, as a high overall success rate can mask specific, critical defects.
Establishing a release policy based on these metrics is essential. A confirmed critical defect, such as a hallucinated resolution, should block deployment. Teams are advised to start with a small set of scenarios, refining the behavioral contract until the team can consistently explain pass or fail decisions.
