Testing Strategies for Generative AI Applications
summary
Defines generative models (VAE, GAN, Transformer/GPT) and claims testing must center on data and concrete metrics: validate quantity, quality, diversity and noise; measure with accuracy/precision/F1 and perplexity or BLEU/ROUGE; apply prompt engineering for LLMs; run model consistency testing and invariance checks, plus failure injection to see how outputs degrade. Recommends metamorphic testing to handle the oracle problem and benchmarking against public models; notes tradeoffs between many strategies (A/B, stress, white-box) and the underlying business case.
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