AI Evaluations and Testing: How to Know When Your Product Works

Des Traynor's Perspective on AI Product Development

Des Traynor, founder of Intercom, provides an in-depth look into the complexities of integrating AI into traditional product development. According to Des, the integration of generative AI introduces a paradigm shift in how products are developed. He emphasizes that while developers love to ship code, the real challenge with AI models lies in understanding whether they work effectively in production environments. Des mentions the concept of "torture tests," which are designed to simulate the most demanding scenarios AI models might face in real-world usage. This rigorous testing is crucial to ascertain the performance and reliability of AI models in production.

Des also highlights the non-deterministic nature of AI, which requires ongoing evaluation and adaptation. In the episode, he states, "You have to do so much, whereas in typical boring bread and butter B2B SaaS… you're assuming that the mathematics worked or whatever." This underscores the need for continuous monitoring and testing to ensure AI systems perform as expected and adapt to changing user inputs and environments. He further elaborates on the need to shift from a deterministic mindset to one that embraces the spectrum of possibilities AI presents, which is a significant departure from traditional software development paradigms.

Moreover, Des discusses the importance of understanding the full lifecycle of AI products, from conception to deployment, and the subsequent need for iterative testing and refinement. He underscores the complexity of building AI products that can evolve over time, reflecting real-world data and user interactions. This dynamic approach requires a deep understanding of both the technical capabilities of AI and the practical implications of deploying these technologies at scale.

Guy Podjarny's Insights on Building LLM-Based Products

Guy Podjarny shares his experiences from Tessl and Snyk on developing LLM-based products. He discusses the inherent difficulties in evaluating AI products, particularly the challenges posed by their non-deterministic behavior. Guy emphasizes the importance of adapting CI/CD processes to accommodate the unique requirements of AI development. He notes, "Some of the tools are quite immature," highlighting the need for innovation and improvement in the tools and methodologies used for AI product development.

Guy also stresses the necessity of empowering developers to work in an AI-first environment. He believes that developers must embrace the ambiguity that comes with AI and learn to navigate it effectively. This involves a shift in mindset from traditional deterministic programming to understanding and managing the probabilities and uncertainties inherent in AI systems. Additionally, Guy highlights the role of continuous integration and continuous deployment (CI/CD) frameworks in streamlining AI development, emphasizing the need for robust testing environments that can handle the unpredictability of AI outputs.

Furthermore, Guy discusses the challenges of maintaining consistency and reliability in AI products, advocating for a holistic approach that combines both automated testing and human oversight. He stresses the importance of fostering a culture of experimentation and learning within development teams, where developers are encouraged to explore new techniques and methodologies to optimize AI performance and usability.

Rishabh Mehrotra on the Importance of Evaluation

Rishabh Mehrotra, Head of AI at SourceGraph, delves into the critical role of evaluation in AI development. He introduces the "zero to one" metric for testing models, which focuses on the effectiveness of evaluation datasets in determining model performance. Rishabh argues that "writing a good evaluation is more important than training a good model," emphasizing that without robust evaluation metrics, developers may not accurately assess the impact of model improvements.

Rishabh also discusses the importance of creating feature-aware evaluation datasets, which are tailored to specific use cases and environments. He points out that industry benchmarks may not always reflect real-world usage, and developers need to develop evaluations that align with actual user experiences and expectations. This approach ensures that AI models are tested against scenarios that truly represent the complexities and nuances of their intended applications.

Additionally, Rishabh highlights the significance of iterative evaluation processes, where continuous feedback and data-driven insights are used to refine and enhance AI models. He advocates for a dynamic approach to evaluation, where metrics are continuously updated and aligned with evolving user needs and technological advancements. This ensures that AI models remain relevant and effective in addressing the challenges of modern software development.

Tamar Yehoshua on Enterprise Search Challenges

Tamar Yehoshua, President of Products and Technology at Glean, explains how Glean manages enterprise search across sensitive data sources. She discusses the challenges of using LLMs as a judge and jury for evaluating AI responses, particularly in environments where data sensitivity is paramount. Tamar highlights the difficulty of ensuring non-determinism in enterprise environments, where users expect consistent and reliable outputs.

Glean addresses these challenges by employing suggestive prompts and structured templates to guide users, thus managing expectations and improving user experience. Tamar shares that Glean has dedicated teams for evaluation and uses LLMs to judge the completeness, groundedness, and factualness of AI responses, providing a nuanced approach to handling LLM outputs. This strategy ensures that AI-generated responses are not only accurate but also contextually relevant and aligned with user expectations.

Moreover, Tamar discusses the importance of transparency and user empowerment in AI-driven enterprise search solutions. She emphasizes the need for clear communication and guidance, enabling users to understand and trust the outputs generated by AI systems. By leveraging a combination of human expertise and automated evaluation tools, Glean ensures that its AI solutions are both robust and user-friendly, catering to the diverse needs of modern enterprises.

Simon Last on Continuous Improvement at Notion

Simon Last, Co-Founder and CTO of Notion, shares Notion's approach to logging failures and creating reproducible test cases. He describes an iterative loop of collecting failures, adjusting prompts, and validating fixes, which ensures continuous improvement of AI capabilities. Simon emphasizes the importance of privacy and user consent in data sharing for evaluation purposes.

Simon highlights the necessity of a repeatable system for managing AI failures and improvements. He states, "You need to make sure that those work and they don't regress," underscoring the importance of maintaining a robust evaluation framework that ensures AI models continue to meet user expectations and adapt to new challenges. This approach enables Notion to deliver reliable and effective AI-driven solutions that enhance user productivity and collaboration.

Additionally, Simon discusses the value of transparency and collaboration in AI development, advocating for open communication and feedback loops within development teams. By fostering a culture of continuous learning and improvement, Notion is able to refine its AI capabilities and deliver innovative solutions that meet the evolving needs of its users.

Evaluation and Testing Strategies Across Industries

The episode brings together common themes and strategies from all guests regarding AI evaluation and testing. A key takeaway is the balance between synthetic tests and real-world scenarios in ensuring AI product reliability. The discussions emphasize the role of human judgment and automation in refining AI models and outputs, highlighting the need for a comprehensive approach to evaluation and testing.

Furthermore, the guests underscore the importance of adaptability and resilience in AI development, encouraging developers to embrace new technologies and methodologies to optimize their AI solutions. By fostering a culture of innovation and experimentation, organizations can harness the full potential of AI to drive business success and deliver exceptional user experiences.