Lyssn

Begin with one bounded use case matching Lyssn's core job: AI platform for analyzing behavioral-health conversations to support clinician training, quality measurement, supervision, and evidence-based care. Provide only the data, files, account connections, or context necessary for that test and define the expected output before relying on the system.

Run a representative example and compare the AI result with the source material or the existing human process. Verify factual claims, extracted fields, calculations, citations, classifications, or generated content as appropriate to the workflow. Record the kinds of inputs that cause weak or inconsistent results.

Use least-privilege access for connected systems and keep human review for financial, medical, security, legal, customer-facing, or other consequential outputs. Where the product provides evaluations, confidence signals, audit logs, approval steps, or validation rules, configure them before increasing automation.

After the workflow is dependable, standardize reusable prompts, schemas, templates, policies, or integrations where supported. Re-test after model or product updates because behavior, limits, pricing, and quality can change.