What makes testing AI agents different from testing regular software?
How do you design an evaluation strategy for an AI agent or LLM that effectively covers both its intended functionalities and its potential failure modes (such as hallucinations or biases)?
How do you best use the insights gleaned from AI agent evaluations, including identifying unexpected behaviors or ethical concerns, to effectively guide the development process and make sure of continuous improvement and reliability?
Are the principles of evaluation-driven development transferrable if instead of LLM, a Hierarchical Reasoning Model (HRM) is involved?
How do you make sure test datasets are diverse enough for fair evaluation?
Datasets - production data may be a challenging ask due to security issues, How do you manage this w clients?
Regarding semantic evaluations, how do you balance human in the loop vs LLM-judge
In today’s AI-driven world, testers need to focus on validating how AI models behave in real-world situations. It’s not just about ticking off test cases; it’s about making sure the AI works reliably, handles edge cases well, and stays fair and safe. Since AI can be unpredictable, ensuring that it behaves ethically and performs as expected in diverse scenarios is key to maintaining quality.
To determine if your evaluation metrics and test datasets truly reflect real-world usage, it’s key to mix up your data sources and use stratified sampling. This ensures you’re testing under various conditions.
Also, build your tests around real-world scenarios, so you’re not just checking for the basics, but for things that could happen in the wild. Keep reviewing and updating your datasets to stay aligned with changing user behavior. And don’t forget to get input from domain experts—they’ll help ensure you’re covering all the right bases.
When AI behavior is non-deterministic, figuring out whether a test failure points to a real issue or just a random outcome can be tricky. A good approach is to define clear acceptance criteria that account for this randomness.
For example, you can set thresholds or confidence intervals to help you gauge what’s within an acceptable range. If a failure falls outside of these expectations, it’s a real issue. But if it’s just a small deviation, it might be a permissible, random outcome. It’s all about understanding the expected variance and knowing when it’s time to act.
To build a solid evaluation framework for an AI agent or LLM, it’s important to go beyond just measuring its performance with traditional metrics like accuracy and F1 score. You also need to assess the agent’s weaknesses, things like hallucinations or bias. A good approach is to combine both quantitative and qualitative assessments.
This means you measure how accurate the agent is, but also dive into how it handles scenarios where bias or errors might pop up. Use real-world examples and adversarial testing to uncover these issues early. This iterative process helps you constantly refine and improve the agent, ensuring it gets better and more reliable over time.
To make sure every new version of your model meets the quality standards before it’s deployed, you need to integrate evaluation pipelines into your CI/CD process.
Think of it as an automated checkpoint system, as soon as a new model version is ready, these checks kick in. The model only moves forward if it passes certain thresholds like performance, fairness, and robustness. This ensures that your models are always up to par, and you’re deploying only the best version every time.
Great question actuallly ! As AI continues to play a bigger role in testing, prompt engineering is definitely becoming an essential skill for test engineers, especially when working with large language models (LLMs). However, it doesn’t mean that test engineers should forget about traditional test case design.
Both are important. Test engineers will need to master the art of crafting clear, effective prompts to guide AI models, while also relying on foundational testing principles to ensure complete and reliable coverage. It’s about blending the old with the new to get the best of both worlds!
When it comes to testing modern AI agents, like probabilistic ones, you can still use classic QA techniques like boundary value analysis (BVA). The key difference is, instead of looking for a single expected result, you’d generate a range of inputs and run the tests multiple times.
This way, you can observe how the AI’s outputs vary based on those inputs, and focus on the statistical distributions of the results. It helps to understand the AI’s behavior more realistically, as its output is often probabilistic rather than deterministic.
When a test for a probabilistic AI agent fails, it’s important to look at the bigger picture rather than focusing on just one unexpected output. In these cases, a bug report should give a clear context, what was the test, what inputs were used, and what was expected versus what was actually observed.
It’s also key to include things like confidence intervals and variations in the input. Instead of pointing out a single odd result, highlight any outputs that fall outside the acceptable range of probabilities. This helps in distinguishing a genuine bug from a random, but expected, outcome in the AI’s behavior.
Great question! When it comes to generating data for edge cases and potential biases, it’s important to be strategic. Here’s what I recommend:
Start by using synthetic data augmentation, which allows you to create new data points that simulate real-world scenarios, especially for edge cases. You can also incorporate adversarial examples, these are intentionally challenging inputs designed to test the system’s strength Then, bring in scenario-driven variations to cover a wide range of situations your model might encounter.
However, simply generating this data isn’t enough. You also need to audit your datasets to make sure you’re not accidentally introducing any hidden biases. One effective way to do this is through cross-validation, where you test your model against real-world patterns to ensure it performs well across diverse scenarios.
In short, the goal is to create diverse datasets, continuously evaluate them, and make sure your models are unbiased while being resilient to edge cases.
When it comes to the value of human empathy and intuition in a world where algorithms and predictive analytics are taking the spotlight, it’s essential to remember what humans bring to the table that AI just can’t replicate. While AI can crunch numbers and predict outcomes, it’s the human touch that excels in interpreting context, understanding emotions, and making ethical decisions.
So, how do we measure this? Well, it’s about tracking the things that matter but aren’t always quantifiable through data, like the real impact on user experience or identifying potential ethical risks that could slip under the radar of an algorithm. These are areas where humans shine because we can tap into empathy, intuition, and complex judgment in ways AI can’t.
By keeping these human-driven metrics at the core, we ensure that the line between AI and human judgment stays clear and balanced. It’s about finding that synergy between the two, so the machines handle the heavy lifting, and we bring the heart and mind to the decisions.
To simulate real-world unpredictability and make sure your AI agents don’t falter in production, it’s important to think beyond just standard tests. You can use stochastic scenario generators, which randomly create scenarios to mimic real-world variations.
Adversarial testing is another great approach, it challenges the system with unexpected inputs or edge cases. Finally, stress testing pushes your agents to their limits, helping you identify any weak spots. This way, you ensure they can handle whatever’s thrown at them in production.
To ensure evaluation stays meaningful when agents interact with each other (not just humans), it’s key to create simulations that involve multiple agents with different roles and goals.
This way, you can assess how they cooperate, resolve conflicts, and maintain stability during interactions. It’s also important to watch for any unexpected behaviors that might emerge during these interactions, as they can reveal valuable insights into the system’s overall performance and reliability.
To keep track of agent reliability without slowing down delivery, it’s crucial to have continuous monitoring in place. Set up performance dashboards that update regularly, so you can quickly spot any issues.
Automated alerts are a game changer here, they’ll notify you instantly if the model starts drifting or fails in some way. Also, don’t forget about lightweight regression tests. They help ensure everything runs smoothly without adding unnecessary delays to the delivery process.