Episode 45: Your AI application is broken. Here’s what to do about it.
Feb 20, 2025
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Joining the discussion is Hamel Husain, a seasoned ML engineer and open-source contributor, who shares invaluable insights on debugging generative AI systems. He emphasizes that understanding data is key to fixing broken AI applications. Hamel advocates for spreadsheet error analysis over complex dashboards. He also highlights the pitfalls of trusting LLM judges blindly and critiques existing AI dashboard metrics. His practical methods will transform how developers approach model performance and iteration in AI.
Prioritizing data analysis over immediate performance metrics is essential for effectively diagnosing issues within AI applications.
Spreadsheet-based error analysis is a practical approach that enables teams to systematically identify and address common failure modes.
Utilizing synthetic data can help simulate user interactions, allowing teams to preemptively test and refine their AI applications before deployment.
Deep dives
The Importance of Error Analysis
Many teams developing AI applications often overlook the significance of error analysis, leading to misunderstandings about what fails within their models. Instead of immediately relying on evaluation libraries, examining failure modes systematically can clarify the root causes of problems. The discussion emphasizes the necessity of understanding data rather than concocting statistics without substance. Emphasizing error analysis as foundational allows teams to focus on concrete issues rather than vague metrics.
Effective Analysis Techniques
Spreadsheet-based error analysis is highlighted as a practical and effective method for clarifying what is going wrong in AI applications. By compiling data into manageable lists, teams can systematically identify common errors such as misclassifications in summarization or chatbot interactions. Specific examples, like incorrect summarization of texts or errors in scheduling requests, illustrate how documenting failures can lead to better insights. This approach facilitates identifying patterns in errors which guides corrective actions effectively.
Utilizing Synthetic Data
When real user data is lacking, generating synthetic data serves as an effective alternative for conducting error analysis. LLMs can be employed to simulate diverse user inputs and interactions, helping to create test cases for areas suspected of weakness. This form of simulation not only allows for testing under plausible scenarios but also aids in identifying failure modes before the application goes live. The conversation stresses the importance of ensuring that applications are rigorously examined using these synthetic scenarios to reinforce reliability.
Iterative Improvement Through Error Insights
Once common failure modes have been identified, teams can iteratively improve their applications by refining prompts and adjusting models based on the insights gained. The idea is to go beyond surface-level fixes and deeply investigate the reasons behind the errors encountered. Regular error analysis can foster a continuous improvement loop, enhancing the overall robustness of AI systems and aligning them closer to user expectations. This process encourages teams to stay vigilant and proactive in managing their applications and user interactions.
Building Trust Through Data Review
Trust in AI applications grows through consistent data review and error analysis rather than solely relying on improved model performance. As model capabilities evolve, ongoing scrutiny of outputs against expected results ensures that applications meet necessary standards of quality. This cyclical process of refining applications based on error reviews cultivates a deeper understanding of user needs and system behaviors. By creatively navigating the complexities of AI outputs, teams can bridge the gap between technical performance and user satisfaction.
Too many teams are building AI applications without truly understanding why their models fail. Instead of jumping straight to LLM evaluations, dashboards, or vibe checks, how do you actually fix a broken AI app?
In this episode, Hugo speaks with Hamel Husain, longtime ML engineer, open-source contributor, and consultant, about why debugging generative AI systems starts with looking at your data.
In this episode, we dive into:
Why “look at your data” is the best debugging advice no one follows.
How spreadsheet-based error analysis can uncover failure modes faster than complex dashboards.
The role of synthetic data in bootstrapping evaluation.
When to trust LLM judges—and when they’re misleading.
Why most AI dashboards measuring truthfulness, helpfulness, and conciseness are often a waste of time.
If you're building AI-powered applications, this episode will change how you approach debugging, iteration, and improving model performance in production.
