Our summary
Not every question needs the same answering model, but choosing a cheaper one can produce an inadequate answer. Seif Ibrahim shows a Java application that asks Jev to judge a request's difficulty. The application then uses its own rules to choose which configured OpenAI model should write the reply.
When Jev's confidence falls below 0.70, code chooses the higher difficulty category of the prediction and a configured backup. It keeps the original prediction and probabilities visible. A separate service checks whether an existing answer is supported by supplied reference material and includes the details needed to answer the question.
These cutoffs are example policies, not proven measures of answer quality. A failed Jev call stops the request rather than using the low-confidence backup. Requests go to hosted Jev, and generating replies also sends them to OpenAI. The separate answer check reports a result; it does not arrange human review or repair.
Key takeaways
- Let AI judge difficulty, but keep the rule that selects a provider model in ordinary code.
- Preserve the prediction even when a backup changes the selected route. Exactly 0.70 meets this example's confidence cutoff.
- Test new prompts and count the cost of acceptable answers. Tuning examples and hypothetical savings do not establish production savings or equal answer quality.