With the emergence of the enterprise-grade generating AI, organizations have entered the rich capabilities of OpenAI, Google Deepmind, Mystral and others. Over time, however, businesses were often seen limiting these models because they were trained on a large trow of public data. Enter Customization – The practice of adapt to a larger language models dello (LLMS) to better suit the specific needs of the business by incorporating its own data and skills, teaching new skills or tasks, or by Putimizing Prompts and Data Recovery Optim.
Customization is not new, but the initial equipment was fairly early, and technical and development teams were often not sure how to do it. It is changing, and customization methods and equipment available today gives businesses more opportunities to make business unique value from AI models.
We surveyed 300 technology leaders mostly in large organizations in various industries to find out how we are searching for the benefit of these opportunities. We also spoke to such a handful of leaders. All of them are customizing AI Models Dells and applications, and they are experiencing their inspiration, the methods and equipment they are using, experiencing difficulties, and sharing the actions they are taking.
Our analysis reveals that companies are moving backwards with customization. They care about its risks, especially revolving around data security, but use advanced methods and equipment, such as redeolation-up-guarantee pay generation (RAG) to realize their desired customization benefits.
This material was created by the hand, insights of the custom material of the MIT technology review. It was not written by the editorial employees of the MIT technology review.
[og_img]
