AI Shift-Left, Test Right

Why AI Needs a Multimodal Test Strategy

It seems as though every organization is infusing artificial intelligence (AI) into its applications and processes these days. And why wouldn’t they? Generative AI and large language models (LLMs) have made it easy to integrate AI and machine learning (ML) capabilities into digital experiences, tools, frameworks, platforms and more.

Some of these AI-based systems are now multimodal, meaning they combine different forms of data to make predictions and draw insights about real-world problems. Multimodal AI trains on and utilizes a combination of images, video, text, speech, audio or numerical data. For example, GPT-4 Turbo makes it possible to feed an image to the model with a text prompt, or request that visuals be used to support an explanatory response.

Beyond the context of AI (pun intended!), multimodality simply refers to activities having different modes. As such, there are many things that can be described as multimodal. Software testing, for example, is multimodal because there are several types of testing, each with formal and informal techniques, methods, and supporting tools. However, for many years the testing industry has been focused on what I consider to be a single mode of testing — pre-production testing. That is, testing that occurs prior to the release of a system or component.

Proponents of the shift-left testing mindset, including myself, advocate for paying attention to quality as early as possible in the software development lifecycle to reduce the cost and impact of late or escaped defects.

For AI-based systems, a pre-release testing strategy that emphasizes shifting left is important. However, because of its dynamic nature, it is equally important to test AI on the right, post-release, commonly referred to as production testing. This is the first article in a two-part series that describes a multimodal testing strategy for AI based on one of our mantra’s at Test IO — Shift-Left, Test Right!

Read the full article here.

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