When developing a Gen AI application, one of the most significant challenges is improving accuracy. This can be especially difficult when working with a large data corpus, and as the complexity of the task increases. The number of use cases/corner cases that the system is expected to handle essentially explodes. 💥
Anindo Banerjea is here to showcase his significant experience building AI/ML SaaS applications as he walks us through the current problems his company, Civio, is solving. Using this case study, he'll also take us through his systematic approach of iterative cycles of human feedback, engineering, and measuring performance. This pragmatic approach is generally applicable, and will provide significant value to developers who are aiming to improve accuracy and speed!
Key Learning Objectives:
- How to leverage human feedback and observability frameworks to detect when the system generates incorrect output and as the basis for accuracy improvements 📈
- How the use of playgrounds integrated into the administrative console of the application can isolate the source of the error 🔍
- How building a robust regression testing framework can ensure that any correction results in improvement for all past inputs, as well as for the new input under investigation 🛠
Register today to save your seat for this insightful new webinar!
📆 November 14, 2024 at 11:00 AM PST, 2:00 PM EST, 7:00 PM GMT
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