What are fallback validation techniques in AI?

What are fallback validation techniques in AI?

Fallback validation techniques are methods used to ensure the reliability of AI systems when primary validation fails.

Can you give me some examples of these techniques?

Sure! Examples include rule-based checks, human-in-the-loop validation, and using alternative models for cross-verification.

How do these techniques improve AI performance?

They enhance performance by providing additional layers of verification, reducing errors, and increasing trust in AI outputs.

Are there any drawbacks to using fallback validation?

Yes, potential drawbacks include increased complexity, higher costs, and the possibility of slower response times.