Testing AI Applications

Tom Shaw

5 years: Software Developer and Content Creator

When working with digital tools and solutions, proper testing of solutions is crucial to avoid mistakes. In this video, Tom Shaw explores the risks of untested solutions in the AI era and how to test your solutions to ensure reliable output.

Working with AI

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Testing AI Applications

4 mins 52 secs

Key learning objectives:

Understand the risks associated with untested solutions
Outline the importance of testing when working with AI and automation tools
Understand how to test solutions effectively
Outline the steps to follow in order to fix mistakes found during testing

Skills:

Information & Data Management

Impacts:

Risk Management

Overview:

Testing is crucial to prevent errors that could lead to serious consequences. To avoid these issues, users should try to break their own solutions, find the weak spots, fix them, and document everything. Testing may seem tedious, but it’s essential to staying ahead of mistakes and keeping their solutions solid. It's all about ensuring reliability before publishing our solutions, especially if it is in a customer-facing environment.

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Summary

What are the risks of untested AI solutions?

AI solutions can return incorrect information, known as "hallucinations". This could lead to potentially dangerous situations, such as giving incorrect advice about allergens or making flawed recommendations. AI solutions are designed to be marketed and trusted but these risks stop us from fully relying on AI. Additionally, mistakes in public-facing solutions can harm reputations and cause operational disruptions. Thorough testing doesn’t guarantee perfection, but it reduces the risk of major failures.

Why is testing important when working with AI and automation?

Testing is crucial when working with AI and automation because it ensures that solutions function properly and meet user expectations. Thorough testing is essential to catch errors early, reducing the risk of failures that can harm a business's reputation or user trust. Especially when systems are designed to be relied on, as in AI-driven tools, testing helps to avoid major mistakes that could lead to serious consequences.

How should AI models and automation solutions be tested?

To test AI models and automation tools effectively, you need to ensure they meet specific requirements and can handle a range of real-world situations. For example, when training an AI model to recognise numbers, you would test it using a dataset with known results, checking whether its predictions match the correct answers.

This principle applies to other solutions, like chatbots or automation systems, where unit testing (or hands-on testing in no-code tools) is used to assess performance. The goal is to simulate real-world scenarios and actively try to "break" the system, ensuring it behaves as expected.

What should you do if something goes wrong during testing?

If you encounter an issue while testing, Tom Shaw suggests following a five-step process:

Identify the impact of the issue
Try to identify the root cause of the problem
Fix the problem once identified
Test again with different examples to ensure consistency
Document the fix so you can reference it later if the issue arises again

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Tom Shaw

Tom Shaw is a self-taught software developer and content creator from Nottingham, UK. He began his coding journey in April 2020 during the COVID-19 lockdowns. He has since worked with various companies on diverse projects, honing his skills and expanding his expertise. Tom is also a content creator, sharing tutorials and insights on platforms like YouTube to demystify coding and make technology more accessible. His project, Code Planner, is a SaaS product designed to assist developers in organising and planning their coding projects.

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