Foundation models are transforming the way companies think about building AI. But foundation models, however powerful, have one drawback: they make mistakes. To be able to leverage them in real-life applications, it’s essential to improve our ability to control their outputs.

In this webinar, we'll explore common mistakes and limitations of models like GPT-4 and SAM, such as low-contrast challenges, limited understanding of proficient data, lack of relevance, and logical consistency.

During this session, you will discover the causes of these errors, including data issues, objective functions, reasoning limitations, and prompt engineering, as well as learn effective strategies to correct these errors and improve the performance of language models in your specific tasks.

Don't miss out on this opportunity to enhance the reliability and accuracy of your models. Register now!