MIT Sloan Management Review Article on The GenAI App Step You're Skimping On: Evaluations
- 6m
- Rama Ramakrishnan
- MIT Sloan Management Review
- 2024
If your organization’s generative AI app didn’t pan out, maybe it’s because your team lacked a strong evaluation process. Here’s a recipe for how to shape one.
If your organization is developing generative AI applications based on large language models (LLMs), you must have a rigorous process to evaluate the quality of each application.
An evaluation process consists of evals — automated tests designed to measure how well your LLM application performs on metrics that capture what end users care about and what is important to the business. Evals speed development by focusing effort on the areas that matter and increase the likelihood of building applications that deliver organizational value. However, the reality is that many teams underinvest in evals. The result: uneven progress and, ultimately, canceled GenAI projects or flawed applications that fail to achieve the business goal.
Business leaders, IT leaders, and developers working hand in hand to develop generative AI apps to solve business problems all benefit from a strong evaluation process. The business and IT leaders gain visibility into the app’s true quality level over the course of the development cycle, and developers can answer critical questions, like “Are we making enough progress?” or “What should the next dev cycle focus on?” or “Is the application ‘good enough’ to deploy?”
About the Author
Rama Ramakrishnan is a professor of the practice at the MIT Sloan School of Management.
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MIT Sloan Management Review Article on The GenAI App Step You’re Skimping On: Evaluations