Adversarial testing is a common practice where you verify the content generated by AI by questioning its accuracy and looking for missing information.

When using AI, approach it with skepticism and ask probing questions to uncover its flaws.

When testing AI agents, it's important to be forgiving and compassionate, as issues may arise from the inputs provided rather than the AI's capabilities.

An iterative approach is essential in working with AI; continuously debugging and refining prompts can lead to more reliable outcomes.

Testing is essential for ensuring that AI systems behave as expected, not just in terms of performance but also in their underlying behaviors.

Our development process involved creating a series of tests to ensure that the AI could accurately respond to legal queries, which was crucial for its effectiveness in practice.

One innovative approach to providing AI with an escape hatch is to allow it to express confusion or request clarification when faced with ambiguous instructions, which can help improve the quality of its outputs.

Testing AI agents involves pre-deployment testing with curated conversation sets to ensure expected responses, followed by ongoing evaluation to adapt to changing user interactions and questions.

Behavioral test coverage in AI applications allows teams to assess whether changes improve or degrade performance, similar to traditional software testing.