Expert Q&A: Ensuring Responsible AI through Gen AI Testing and Evaluation

Generative AI models are advancing rapidly, but with these advancements come the challenge of ensuring responsible and ethical outcomes. Ensuring Responsible AI is essential for creating outputs that are not only functional but also ethical and reliable. Issues like hallucinations, toxicity, and contextual inaccuracy require a unique approach to testing—one that goes beyond traditional software QA.

Understanding the “why,” “what,” and “how” of generative AI testing is crucial to ensuring these systems meet user expectations, comply with legal standards, and uphold the ethical principles essential to Responsible AI. Recently, we spoke with Viswanath Pula, AVP – Solution Architect & Customer Service at ProArch, to explore what Responsible Gen AI Testing entails and the best practices for testing and validating generative AI applications effectively.

What is the difference between Testing Gen AI applications and Testing with Gen AI?

Gen AI Testing focuses on ensuring that generative AI models—such as Large Language Models (LLMs)—generate outputs that are not just functional but also ethical, reliable, and contextually relevant. Gen AI Testing dives deeper into evaluating the behavior of these models when they generate responses. These models are probabilistic, meaning the outputs can be unpredictable and vary significantly based on input and context.

In contrast, AI in testing refers to using AI tools to automate and optimize traditional software testing processes. This includes tasks like test case generation, where AI tools automatically generate comprehensive test cases based on application requirements, ensuring coverage of various scenarios. AI can also review existing test cases to identify gaps, such as missed use cases or edge cases, and suggest improvements.

While AI in testing is about enhancing the traditional testing process, Gen AI testing specifically targets the unique complexities associated with testing generative models.

How is testing Gen AI different from traditional software testing?

Viswanath explains, “Unlike traditional software that typically follows a linear path with expected outputs, LLMs operate on probabilities and can create a variety of responses to the same prompt. This means we can’t just check if the model works; we must assess if it’s producing responses that make sense in context.”

A critical aspect of this is addressing “hallucinations,” where the model might confidently provide information that sounds plausible but is false. Ensuring that models don’t lead users astray with misinformation is where ethical testing becomes essential.

There’s also an urgent need to address toxicity and bias in Generative AI. Testing goes beyond simply generating content; it involves making sure that this content is produced in a way that’s safe and fair for everyone.

Many organizations assume that testing generative AI is just part of the initial development process. What insights can we offer to help them understand the ongoing need for Gen AI testing?

Many organizations think testing generative AI is only necessary during initial development, but that’s a misconception. Generative AI models, like large language models (LLMs), can behave unpredictably across different inputs, making ongoing testing essential to avoid unexpected outcomes. Every model update requires retesting to confirm expected performance.

Another important factor is that the model itself is only a small part—around 20-30%—of the entire AI solution. Other components, such as input processing, user interface (UI), and knowledge bases, play critical roles. For instance, in applications like chatbots, if a new HR policy document replaces an older one, testing ensures the system accurately incorporates the updated content and responds accordingly.

In short, Gen AI testing is a continuous process to maintain accuracy, relevance, and compliance.

For organizations/teams who haven’t thought about testing their Gen AI models, what are some tangible benefits they might not be aware of?

If you haven’t prioritized testing for your Gen AI models, here are some valuable benefits you might be missing:

• Building User Trust: Consistent, reliable responses from tested models build user confidence in your AI.
• Handling Unseen Scenarios: Testing across diverse scenarios prepares the model for unexpected real-world inputs, enhancing adaptability.
• Smooth Model Updates: Regular testing ensures new model versions integrate seamlessly, maintaining or improving performance.
• Reducing Bias and Toxicity: Testing helps mitigate bias and catch harmful outputs, supporting fair and respectful interactions.
• Seamless Integration: Verifying compatibility between the model and all connected components ensures a cohesive user experience.
• Continuous Improvement: Ongoing testing keeps models aligned with new standards, driving performance optimization over time.

Testing isn’t just a checklist—it’s essential for creating ethical, reliable AI that delivers real value.

Can we implement continuous testing in testing Gen AI? If yes, how?

Absolutely! Continuous testing is not just feasible; it’s essential for Gen AI models.

Here’s how to implement continuous testing effectively:

• Automated Testing Pipelines: To implement continuous testing within the DevOps pipeline, integrate automated testing tools and systems that run tests on your generative AI model at various stages of development. This integration ensures that the model’s performance is consistently monitored across essential metrics like accuracy, quality, and behavioral consistency. You can detect potential issues early in the development cycle, allowing for faster feedback and resolution.
• Drift Monitoring: Continuous testing tracks “drift,” which can occur as the model’s responses change over time. Regular assessments help ensure that the model remains aligned with expected behavior and performance standards.
• Real-World Simulation: Incorporate tools like Azure DevOps to simulate real-world scenarios. Testing in conditions that mimic production environments, such as multi-factor authentication for chatbots, ensures the model behaves as expected in actual usage.
• Overnight Batch Testing: Large-scale tests can be run overnight, generating a detailed dashboard by morning. This includes pass/fail statuses, explanations for failures, and detailed logs to help teams quickly identify and address any issues.
• Prompt Engineering: Continuous testing in Gen AI also involves refining prompts to optimize model outputs. By crafting more precise and context-specific prompts, the model can be guided toward producing clearer, more accurate, and relevant responses. This refinement process helps ensure the AI performs well across varied scenarios and adapts to changing requirements.
• Human in the loop: While automation covers much of the testing, human oversight remains critical. Subject matter experts review the AI’s outputs, confirming accuracy and relevance. This human element helps catch any “hallucinations” or unexpected outputs, ensuring final accountability and quality alignment with domain standards.

By integrating continuous testing, organizations can keep their models sharp and aligned with user expectations.

Are there any industry-approved frameworks for testing generative AI models?

Testing generative AI models calls for a unique approach, where ethics, reliability, and bias come into play alongside performance. Fortunately, there are several frameworks and best practices to guide this process.

Organizations like NIST and OpenAI have developed guidelines specifically for testing generative AI, focusing on ethical standards and performance. Among the open-source frameworks, DeepEval and RAGAS are especially valuable. DeepEval offers a comprehensive suite for evaluating the quality of AI outputs, enabling teams to assess aspects like coherence, relevance, and consistency of responses. RAGAS, on the other hand, provides robust tools for testing retrieval-augmented generation (RAG) models, focusing on accuracy and contextual relevance when models pull information from various sources.

With over 30 open-source frameworks available, teams have a broad range of tools to experiment with and select the best fit for their specific testing requirements, ensuring their generative AI models are both high-quality and aligned with industry standards.

What are the key challenges in implementing Gen AI testing at a scale?

Scaling Gen AI Testing presents some unique challenges. Viswa outlines a few of the most pressing issues:

1. Resource-Intensive Processes: Gen AI testing demands significant computational resources, making it crucial to build and maintain scalable infrastructures. This includes ensuring adequate processing power to test large models across various scenarios and workloads, which can be resource-intensive and complex.
2. Unpredictable Responses: Gen AI models often produce varied and unpredictable outputs, making it difficult to establish consistent testing criteria. Unlike traditional applications, where logic is predefined, the dynamic nature of Gen AI responses requires advanced techniques for accurate model behavior assessment.
3. Ethical Risks: Ethical considerations, such as biases and toxicity, are significant concerns in Gen AI. Ensuring compliance with ethical standards demands comprehensive strategies for identifying and mitigating these risks to maintain fairness, transparency, and accountability within AI systems.
4. Synthetic Data Generation: Gen AI models require large volumes of high-quality data for training and testing. Gathering and managing this data, particularly for niche use cases, can be challenging. To overcome limitations in real-world data, organizations often turn to synthetic data. However, generating synthetic data that accurately mirrors real-world scenarios can be complex, and it may not always provide the same level of insight as actual user-generated data.

To tackle these challenges, Viswa recommends creating a streamlined approach with clear success metrics. “Focus on the high-risk areas and prioritize your testing efforts there. A strategic approach helps ensure Gen AI applications are both reliable and cost-effective,” he advises.

Make sure your Gen AI is application truly reliable, safe, and compliant

Partner with Enhops to ensure your Gen AI models perform at their best. Through our four-week ImpactNow engagement, we’ll assess your AI application’s performance, reliability, and compliance, with expert support at every step.

Not sure where to start? Watch our exclusive on-demand webinar on Gen AI Testing for insights and expert tips to help you get your Gen AI app for the real world.