Sure! Here’s the translation into American English:
In a recent session, Srinath Godavarthi, Director and Divisional Architect at Capital One, focused his talk on optimizing performance and output quality of generative artificial intelligence. The purpose of this reflection was to enhance effectiveness for both customers and businesses in an increasingly competitive and technological environment.
Godavarthi emphasized the importance of base models and addressed the challenges they present, such as variability in the quality of their results and the so-called “hallucinations,” which are errors generated from noisy training data. These issues underscore the need for greater care in training processes and in the selection of the data used.
During the talk, the executive presented four key strategies for improving the performance of artificial intelligence models. These are prompt design, retrieval-augmented generation (RAG), fine-tuning, and building models from scratch. Each of these techniques offers particular advantages; for example, prompt design allows for quick and effective improvements, while fine-tuning facilitates specialized adaptations that can be essential for specific tasks. The choice of the most suitable strategy will depend on the specific use case and the complexity of the tasks involved.
This analysis not only highlights the inherent challenges of generative artificial intelligence but also presents practical solutions for maximizing its effectiveness. Addressing these issues is essential if we want to fully leverage the potential of these technologies in various applications, from customer service to specialized content creation.
via: MiMub in Spanish
