At Certus3, we’ve learned that you can’t trust what you don’t test—and that’s especially true for Generative AI (GenAI) and large language models (LLMs). While these models are powerful, their behavior can be inconsistent, unpredictable, and occasionally very wrong. In our work building AssuranceAmp, we’ve found that rigorous testing is just as important as getting the data and orchestration right.
The Unique Challenge of Testing GenAI
Traditional software development follows clear testing paradigms. You write unit tests, integration tests, and user acceptance tests (UAT). But when you’re dealing with an AI that generates responses based on vast amounts of data, the traditional approaches don’t fit. GenAI doesn’t return deterministic results—it draws from probability distributions and context, which means even a small change in the input can lead to a completely different output.
So how do you test something that doesn’t behave the same way twice?
Our Solution: AgentAmp, the Testing Platform for AI
To tackle this challenge, we built AgentAmp, an internal tool that allows us to rapidly test and refine our LLM-agent interactions. AgentAmp is designed to do much more than orchestrate workflows; it gives us the ability to continuously test every aspect of the AI’s behavior, from data retrieval to multi-step reasoning.
With AgentAmp, we can:
- Simulate multiple real-world scenarios to see how the AI handles edge cases and common use cases.
- Track the performance of various prompts, data flows, and model versions.
- Refine agent behavior by adjusting interactions, feedback loops, and context feeds.
Testing isn’t just a one-off task—it’s an ongoing process, and AgentAmp allows us to run regression tests every time a model is updated or a new data source is integrated.
Testing LLM Responses: The Key Factors We Focus On
Through our testing process, we’ve identified several critical areas that we pay close attention to when evaluating the behavior of LLM-powered applications like AssuranceAmp:
1. Accuracy and Relevance
- Challenge: LLMs can generate impressive responses, but they can also hallucinate, giving factually incorrect or irrelevant answers.
- Testing Focus: We test how well the AI pulls in the correct context, evaluates it, and generates answers that align with real-world facts, metrics, and domain knowledge.
- How We Test: By comparing the model’s output against a ground truth set of responses, and checking if the recommendations are actionable and aligned with business goals.
2. Consistency
- Challenge: LLMs can sometimes be inconsistent. Ask the same question twice and you might get different answers. This is particularly challenging when the AI is supporting critical business decisions.
- Testing Focus: We assess whether the model provides stable, consistent outputs over time, ensuring that it behaves predictably for the same input in identical conditions.
- How We Test: We input the same question multiple times, using varied forms of context and input, and measure the variance in responses.
3. Handling Ambiguity
- Challenge: GenAI often has to handle ambiguity—uncertain inputs, vague queries, or complex questions that don’t have a single clear answer.
- Testing Focus: We look for how well the AI acknowledges uncertainty and provides actionable responses in these situations. The model should flag ambiguity and ask clarifying questions or outline the limits of its certainty.
- How We Test: By feeding in questions that are deliberately ambiguous or involve complex multi-step reasoning, and evaluating whether the AI’s response is thoughtful and reasonable.
4. Speed and Latency
- Challenge: When operating in a business environment, the speed of the AI’s response can be as important as its accuracy.
- Testing Focus: We measure response times under various load conditions and assess whether the system can handle real-time interactions.
- How We Test: Using performance testing tools, we simulate high-demand scenarios and observe how the model’s performance holds up.
Test Early, Test Often: Continuous QA in GenAI
One of the key insights we’ve learned during this journey is that testing should start early and happen often. As LLMs are inherently probabilistic, small tweaks in the input data or updates to the underlying model can dramatically alter output. With AgentAmp, we run continuous testing—ensuring that every interaction, every prompt, and every feedback loop is validated before it hits our end users.
Our testing process includes:
- Automated regression testing after each model update.
- Simulated user interactions to assess how the AI handles various scenarios and inputs.
- Performance benchmarks to ensure responsiveness remains high under load.
- Human oversight for final verification—ensuring the model’s outputs align with our core business goals.
This continuous testing pipeline helps us avoid nasty surprises once the system is deployed in production.
Conclusion: Testing is a Never-Ending Journey
The journey to incorporating AI into a real-world business application is full of challenges, but testing doesn’t have to be one of them. By using AgentAmp to guide our testing processes, we ensure that our platform—AssuranceAmp—remains reliable, accurate, and aligned with our business objectives.
But testing doesn’t end with just launching the platform—it’s ongoing. With each iteration, update, or model improvement, we must ensure the system continues to perform at the highest level.
In the next post, we’ll dive into the challenges and strategies around feeding real-world business data into an LLM, and how we structure information to make it both accessible and meaningful for AI models. Stay tuned for more insights on building GenAI-powered business solutions.
Ready to experience reliable, enterprise-grade GenAI that you can actually trust? Discover how AssuranceAmp can transform your business operations with AI you can depend on. Sign up for a free demonstration today and see firsthand how our rigorously tested AI solutions deliver consistent, accurate results for organizations like yours. Don’t just hope your AI works—know it does. Schedule your free AssuranceAmp demo now and take the first step toward AI-powered confidence.
