Before you deploy a generative AI workflow, ask these questions:
Task definition
Is the task clearly defined?
Do we know what a good output looks like?
Data
Do we have representative examples?
Are domain edge cases included?
Evaluation
Do we have a test set?
Are we measuring quality beyond fluency?
Model comparison
Have we benchmarked more than one model?
Have we compared zero-shot, retrieval, and fine-tuned approaches?
Failure mapping
Do we know the top ways the system fails?
Do we know which failures are acceptable and which are not?
Human oversight
Can experts review outputs?
Is there a feedback loop for improvement?
Deployment
Are privacy and permissions handled correctly?
Do we have monitoring and logging?
If the answer to most of these is no, the workflow is not ready yet.
The fastest way to lose confidence in AI is to deploy without measurement. The fastest way to build trust is to evaluate first.
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