What is Adversarial Testing for Generative AI | How to use it?

With the growth of AI and LLMs there’s an everlasting need of its testing. This testing does not only focus on the correctness of the data used while training a model or in its output. But instead how it recognises and uses it to generate.

With the growth rate of AI models, its challenging to track the progress hence, Quality can be plausible during such contexts.

Let’s quickly understand some common terms, that I’ll be using through-out this blog:

1. Large Language Model (LLMs): Can be used to generate data, images, videos etc. and are trained over years to do so. As the tasks mentioned are very resource intensive hence they require high processing and storages.
2. Machine learning (ML): is the field of study of programs or systems that trains models to make predictions from input data. ML powers some of the technologies that have become integral to our daily lives, including maps, translation apps, and song recommendations, to name a few.
3. Artificial intelligence (AI): is a non-human program or model that can perform sophisticated tasks, such as image generation or speech recognition.
4. Prompt engineering (PE): is the art of asking the right question to get the best output from an LLM. It enables direct interaction…