Define the Workload
Write the user-facing task as a sequence of steps with explicit inputs, outputs, and decision owners. Separate deterministic work, such as validating required fields or applying business rules, from work where generated or interpreted content is useful. For a hypothetical support inbox, one step might classify a request and extract a product identifier; another might draft a response for review. Define what happens when the identifier is absent, the request is ambiguous, or the draft lacks evidence. Keep high-impact decisions behind an appropriate human or deterministic check instead of treating a plausible response as proof.
Map Modalities and Data
Inventory the content the application actually receives: text, images, audio, or a justified combination. Make a small, representative evaluation set for each input type, including ordinary cases and likely edge cases. For images, consider blur, cropping, occlusion, and irrelevant content. For audio, include background noise, silence, and varied speaking conditions. For text, include abbreviations, misspellings, mixed context, and missing fields. Decide which data is necessary, check permissions and retention requirements, and remove or protect sensitive information where appropriate. If supported input formats, limits, or handling details are unclear, consult the current main platform documentation rather than assuming behavior.
Evaluate Outputs
Create examples with human-reviewed expected outcomes, including cases that should be rejected or routed for clarification. Keep some examples aside from prompt and parser development so that repeated tuning does not make the final check misleading. Define criteria tied to the application: for extraction, inspect missing fields, incorrect values, and schema violations; for generated replies, inspect unsupported claims, omissions, and inappropriate certainty. Run your own checks after changes to prompts, parsing, or model configuration, and record the actual outcomes, including failures and review decisions. NexoToken provides AI model API access; account platform credits and usage history can help with operational planning and review. Usage history is not evidence of output quality, and no evaluation result should be inferred before the checks are run.
Plan Throughput and Failure Paths
Estimate load from expected request patterns, input size, concurrency, retries, and any human review step. Treat the estimate as a starting assumption, then update it from your own observed application traffic and actual usage records. Set bounded retry behavior with backoff for transient timeouts or rate limits, and define what the application does after retries are exhausted. In a hypothetical transcription workflow, if a required entity is missing or uncertain, mark the result for review and prevent dependent irreversible actions until it is resolved. For malformed output, validate the structure and route it to a repair or fallback path; for unavailable service, queue work or offer a clear manual route. Log request identifiers, failure categories, and dispositions while avoiding unnecessary sensitive payloads. Verify these behaviors in the application’s own checks before relying on them.
Sources & further reading
NexoToken platform and community
Platform website: https://www.nexotoken.net/
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