The rise of generative AI (Gen AI) applications is poised to revolutionize the way businesses operate, offering unprecedented capabilities in areas like customer service, content creation, and data analysis. However, the complexity and unpredictability of non-deterministic applications present unique challenges when it comes to testing and evaluation. Unlike traditional software, Gen AI applications produce outputs that can vary widely depending on the free-text input, making standard testing methods insufficient.
To ensure that Gen AI applications perform reliably, safely, and effectively, it is important to have a clear and strategic evaluation plan. This involves not just testing functionality but also assessing the robustness, reliability, and compliance of the AI system within its specific context. A well-thought-out testing strategy is essential to unlock the full potential of Gen AI applications and deliver value to users.
While the news is full of benchmarks regarding the latest and most powerful large language models (LLMs), standard AI benchmarks and generic evaluation approaches often fall short when applied to company-specific Gen AI applications. There is a significant difference between evaluating the general capabilities of an AI model and assessing how well it serves within a specific context and use case, given a unique application implementation. For instance, knowing how well a model like GPT-4 answers math questions provides little insight into its ability to summarize legal documents or draft business emails as part of a product.
Context-specific evaluations allow a focus on what truly matters for Gen AI applications. They help identify relevant metrics that align with users' needs and business objectives. By tailoring evaluations to an application's context, issues can be uncovered that generic tests might miss, such as domain-specific misunderstandings or compliance violations in a particular industry. This approach ensures that AI applications are not just powerful but also appropriate and effective within their intended use cases.
Setting Clear Evaluation Goals
Before diving into the testing process, it is essential to clarify evaluation goals by defining what success looks like for a given Gen AI application and setting key metrics that align with respective objectives. These goals should encompass aspects of robustness, reliability, and compliance.
Robustness involves testing for adverse and unexpected harmful inputs to ensure that the application can handle them gracefully without producing inappropriate or harmful outputs.
Reliability focuses on verifying that the AI performs its intended functions consistently and adheres to the desired scope and restrictions.
Compliance entails assessing the AI system for bias, toxicity, and adherence to ethical standards and regulations, among others.
Involving stakeholders and experts from different domains can help refine these goals. Collaborating with the team to determine which risks to evaluate and considering the inclusion of a "Panel of Domain Experts" whose judgement is essential for the success of the AI product and can provide valuable insights. Clear evaluation goals provide direction and purpose to testing efforts, ensuring that they contribute meaningfully to the application's development.
Preparing a comprehensive test set that aligns with the established evaluation objectives is key. It needs to cover each aspect with relevant test cases and different approaches to confirm that desired behavior is exhibited and to provoke undesired behavior.
Selecting appropriate evaluation methods is crucial for effectively assessing the AI application. Evaluations can generally be categorized into objective and subjective methods.
Objective evaluations involve quantifiable measures, such as whether a response meets a specific format or matches a ground truth example. These are more traditional, can often be automated, and are useful for testing functional correctness and adherence to specifications.
Subjective evaluations assess qualities like friendliness, helpfulness, or alignment with desired response characteristics and brand voice—areas where human judgment commonly excels. In many cases, AI models can assist in these evaluations through techniques like "LLM-as-a-Judge."
Balancing both objective and subjective evaluations provides a comprehensive understanding of the AI application's performance. Designing test cases that cover the boundaries of desired behavior and each restriction ensures that the AI system does not produce unexpected or harmful outputs. Without such evaluations in place, companies risk significant consequences, including reputational damage from offensive or inaccurate outputs, liability issues due to compliance failures, and operational inefficiencies caused by unreliable AI behavior. By choosing methods that align with the evaluation goals and the team's expertise, organizations can mitigate these risks, ensuring their applications are both effective and trustworthy.
Traditional evaluation metrics like BLEU or ROUGE are often insufficient for Gen AI applications, as they may not capture the nuances of specific use cases. Developing custom evaluation criteria tailored to an application's context is key.
This begins with defining the capability and risk boundaries relevant to the application. These boundaries represent specific AI capabilities that need to be achieved and, if exceeded, could lead to unacceptable outcomes. For example, in a healthcare application, a capability boundary might involve the AI providing medical advice beyond its scope, which could pose safety risks.
Custom evaluation criteria should reflect both the functional and non-functional requirements as well as the ethical considerations of the application. They should be designed to test the AI system's performance in areas like:
In a healthcare advice application, a custom evaluation metric could be answer completeness, which measures whether the AI's response fully addresses the user’s query. An answer may be factually accurate but incomplete, potentially missing critical details that impact decision-making.
To assess the completeness of the response, the following simplified prompt could be used:
Prompt:
"Evaluate the completeness of the following response to a healthcare-related query. Consider whether the answer includes all relevant and critical information for the user’s situation without adding unnecessary or misleading details. Provide a score from 1 to 5, where 1 is very incomplete and 5 is fully comprehensive. Justify your score in 1-2 sentences."
Response to Evaluate:
"If you experience severe chest pain, you should seek medical attention immediately. Avoid any physical exertion and call emergency services if the pain persists."
Example Output from LLM-as-a-Judge:
By using this approach, organizations can leverage LLMs to evaluate other LLMs, ensuring their outputs align with the application’s requirements and effectively serve the intended purpose.
Testing is not a one-time activity but an ongoing process that should span the entire software development lifecycle. Consistently reviewing new test sets and iterating based on the findings is crucial for maintaining and enhancing the AI application's performance.
Continuous evaluation allows for monitoring how Gen AI applications behave in real-world conditions and adapting to changes over time. It helps detect issues that may only emerge during actual usage and unforeseen user interactions. By integrating evaluation processes into the development workflow, testing can be automated, and experimentation can be accelerated, much like continuous integration (CI) in traditional software development.
Moreover, continuous monitoring supports transparency and builds public confidence. Independent evaluations, particularly by third parties, provide objective verification of the AI system's capabilities and safety. They help eliminate real or perceived biases and contribute to advancing the science of AI evaluations by providing insights from multiple perspectives.
Planning and executing effective testing for Gen AI applications is a complex but necessary endeavor. By focusing on context-specific evaluations, setting clear goals, choosing appropriate evaluation methods, developing custom criteria, and embracing continuous monitoring, it is possible to ensure that AI applications are robust, reliable, and compliant with ethical standards.
A strategic evaluation plan not only helps identify and mitigate risks but also enhances the overall quality and effectiveness of the AI application. It enables the delivery of a product that meets users' needs and expectations while operating safely within its intended context.
As the field of AI continues to evolve, investing in a thoughtful and thorough testing strategy will remain a cornerstone of responsible and successful AI deployment. By unlocking the full potential of AI applications through strategic evaluation, products are positioned for long-term success in a rapidly changing technological landscape.