How to Evaluate Generative AI Applications for Responsible AI Standards - Webinar Insights

Generative AI opens huge possibilities—but with that comes a big question: is your application ready for production? Skipping proper evaluation doesn’t just risk poor performance; it can lead to biased results, security issues, and damaged trust.

And when it comes to Gen AI, being production-ready means much more than checking if inputs and outputs technically “work.”

You need to ask:

• Are the outputs clear, concise, and contextually helpful?
• Is the model hallucinating or generating unreliable information?
• Has the training data been properly sanitized to avoid privacy leaks or embedded bias?

Functional tests are just the start. True readiness lies in how trustworthy, safe, and responsible your AI system is in real-world scenarios. It’s a lesson some teams are learning the hard way. At AI startup Cursor, a customer support bot went off-script—leaking internal information, logging users out, and even responding with profanity. The backlash was swift: subscriptions were canceled, influencers called out the lack of transparency, and user trust eroded quickly.

The incident is a powerful reminder that Gen AI’s non-deterministic nature makes it prone to hallucinations and unexpected behavior. Without rigorous evaluation and safeguards, even well-intentioned AI can spiral into very public failure.

To unpack what this really means, we spoke with Viswanath Pula, AVP – Solution Architect & Customer Service, at ProArch.

Q: What are the risks of deploying Generative AI applications without properly evaluating them for responsible AI?

A: You’re looking at a wide range of serious issues:

• Bias & fairness problems – leading to discriminatory outputs.
• Security vulnerabilities – which could open doors for attacks or manipulation.
• Privacy concerns – from mishandling sensitive data.
• Inaccurate predictions – that result in business losses or damaged reputations.
• Legal & compliance risks – including penalties, fines, and user trust erosion.
• User dissatisfaction – caused by confusing, irrelevant, or unhelpful outputs.

Q: What makes testing Generative AI more complex than traditional software?

A: Traditional testing is clean. You feed in A, expect B, and check if it matches. Done.

With Gen AI, it’s not that straightforward.

The model generates outputs based on patterns it’s learned—not fixed rules. So even with the same input, you might get different results. And you can’t always tell why it made the choices it made.

That’s why Gen AI is referred to as a black box.

• The algorithms are complex and often opaque.
• There’s no clear logic path to trace.
• Debugging and fine-tuning require a lot of guesswork.

Q: How do you ensure a Gen AI application meets responsible AI standards?

A: The goal of Gen AI evaluation is to understand how well it works and whether it’s doing so in an ethical, transparent, non-bias way.

Ensuring Responsible AI means assessing key qualities like:

• Faithfulness – Is the information factually accurate?
• Relevance – Does the response directly answer the prompt?
• Context precision – Is the answer grounded in the correct supporting information?
• Bias and toxicity – Are they safe, ethical, and free from harmful content?

Manual evaluation, while helpful, is time-consuming, inconsistent, and doesn’t scale. Reviewing just 200 prompts can take a week or more—an unmanageable task when teams are overseeing multiple Gen AI applications across departments.

Q: What does a strong responsible AI testing framework include—and how does it help?

A:  A responsible AI framework is what keeps your generative AI grounded. It provides a consistent way to measure how your model is performing, uncover weaknesses, and improve outputs over time. Which is what AIxamine does.

A well-rounded framework typically includes:

• Input prompts with expected outputs and context—so there’s a clear reference point for evaluation.
• A critique LLM (like Azure OpenAI) to review and analyze outputs using defined evaluation criteria.
• Evaluation tools like DeepEval and RAGAS to score key metrics such as faithfulness, relevance, hallucination, and contextual accuracy.
• Quality profiles that define what “good” looks like—setting rules, metrics, and thresholds based on the use case.
• Quality gates that act as checkpoints—ensuring only models that meet predefined standards for safety, accuracy, and bias can move forward in the development lifecycle.
• A reporting layer (such as Power BI) to visualize, track, and share evaluation results across teams.
• CI/CD pipeline integration, so Gen AI evaluation becomes a continuous, automated process—not a one-off review before release.
• Integration with your tech stack, including React for front-end delivery and SQL Server for securely storing prompts, evaluation results, and model history.

Q: How does ProArch’s AIxamine work?

AIxamine makes it easy for AI/ML teams to evaluate Gen AI applications for responsible AI—whether you’re testing a new model or monitoring one in production. It fits right into your development workflow and brings structure to how you test, measure, and improve your AI.

1. Start with prompts—you define real-world questions or tasks you expect the model to handle.
2. Run the model to generate responses for those prompts.
3. Evaluate the output using tools that score based on metrics like faithfulness (is it factually, right?), relevance (does it answer the question?), and bias or toxicity (is the response safe and appropriate?).
4. Review and report the results in dashboards or reports so teams can see what’s working and what’s not.
5. Fine-tune or retrain the model based on what you’ve learned.
6. Repeat the process to validate improvements and keep the model aligned with your quality standards.

With AIxamine, you’re not just checking a box—you’re building Gen AI that works, scales, and earns trust.

ProArch: Your AI Partner

Navigating the world of generative AI can be complex, but with ProArch’s AI consulting services, you can ensure your systems are effective and responsible.

Whether you’re starting fresh or refining existing AI systems, ProArch’s team is here to guide you through every step—from identifying use cases to ensuring ethical implementation. Explore the   for more insights or contact ProArch for guidance on your AI strategy.

Looking to evaluate your Gen AI systems? Try AIxamine, ProArch’s framework to ensure accuracy, safety, and responsible deployment.