Index
An AI pilot may seem excellent if given only easy examples. The sample needs to represent the variety, exceptions, and conditions the operation will encounter after deployment.
How to evaluate this decision
Separate data used to adjust the solution from that reserved for evaluation. Include recent, rare and out-of-scope cases with adequate protection of information. Define the expected response or review method before observing the output. Choosing only successful outcomes prevents you from reliably comparing alternatives.
Criteria for comparing proposals
- Representation: cover origins, formats and degrees of difficulty relevant to the process.
- Reference: record human decisions and disagreements that require clarification of the rule itself.
- Interruption: define failures that block expansion and situations in which the tool must request help.
A scenario to discuss with the supplier
Hypothetical example: the solution classifies well-formatted documents, but production receives cropped images. The pilot must include this condition to measure the need for revision, not exclude it as an operational detail.
What to validate upon delivery
Present results by case type, including errors and unprocessed samples. Check that the evaluation data has not been used repeatedly to adjust the solution until you memorize the test.
Prepare the conversation about the project
Quantum9 can structure the pilot and its evaluation. Report variety of inputs, impact of errors, and reviewers to decide whether the result warrants deployment.
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