Descriptions:
Fahd Mirza walks through how to set up and run OpenAI’s evals framework on a practical real-world task: classifying the causes of inventory count discrepancies in a warehouse environment. Rather than treating evaluation as an abstract concept, the video grounds everything in a working Python example where a dataset of SKU-level inventory events is passed through an OpenAI model configured as an inventory control analyst, and the model’s predictions are compared against human-labeled ground truth.
The setup portion covers the full environment on Ubuntu — creating a Conda virtual environment, installing the openai-evals and pandas packages via pip, configuring an API key, and running the classification pipeline end to end. Mirza then walks through interpreting the results: the model correctly identified some categories like ‘misplaced’ but showed a clear bias toward that label, failing to distinguish subtler causes like theft, damage, or returns. Accuracy scores and confusion-matrix-style output are used to make the failure mode visible.
The broader lesson is that structured evaluation moves AI validation from intuition-based spot checks to a measurable, repeatable process. Teams can use frameworks like OpenAI evals to compare prompt versions, detect regressions when a model updates, and build genuine reliability metrics over time. The video is a useful starting point for developers who need to move beyond manual QA and into data-driven model assessment for production use cases.
📺 Source: Fahd Mirza · Published May 10, 2026
🏷️ Format: Tutorial Demo
