Rama Ramakrishnan

1 month ago | tribunecontentagency.com | Rama Ramakrishnan |Elizabeth Heichler

MIT Sloan(AUTHOR’S BIO)Rama Ramakrishnan is a professor of the practice at the MIT Sloan School of Management, specializing in the practical application of predictive and generative AI techniques for industry problem-solving and the development of intelligent products and services.

Dec 5, 2024 | tribunecontentagency.com | Rama Ramakrishnan

Rama Ramakrishnan is a professor of the practice at the MIT Sloan School of Management. 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.

Dec 5, 2024 | sloanreview.mit.edu | Rama Ramakrishnan

Topics Column Our expert columnists offer opinion and analysis on important issues facing modern businesses and managers. More in this series Carolyn Geason-Beissel/MIT SMR | Getty ImagesIf 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.

Nov 4, 2024 | tribunecontentagency.com | Rama Ramakrishnan

Rama Ramakrishnan is a professor of the practice at the MIT Sloan School of Management. When large language models exploded onto the scene in 2022, their powerful capabilities to generate fluent text on demand seemed to herald a productivity revolution. But although these powerful AI systems can generate fluent text in human and computer languages, LLMs are far from infallible. They can hallucinate information, exhibit logical inconsistencies, and produce irrelevant or harmful outputs.

Nov 4, 2024 | sloanreview.mit.edu | Rama Ramakrishnan

A Practical Guide to Gaining Value From LLMs Getting a return from generative AI investments requires a systematic approach to analyzing appropriate use cases. Michael Austin/theispot.comWhen large language models exploded onto the scene in 2022, their powerful capabilities to generate fluent text on demand seemed to herald a productivity revolution.