Explore how Salesforce addresses AI testing challenges, enhances app reliability, and leverages user feedback to improve AI model outputs.

The Salesforce Engineering Blog offers a deep dive into Salesforce technologies, providing technical insights, best practices, and real-world examples. Developers can explore topics such as Salesforce development, architecture, and integrations, gaining  knowledge to build innovative solutions on the Salesforce platform. With contributions from Salesforce engineers and industry experts, the blog serves as a resource for the Salesforce community.

Salesforce Engineering

Erwin Karbasi discusses the Salesforce Central Evaluation Framework (SF Eval), an innovative tool for assessing AI model performance. This layered platform integrates traditional machine learning metrics with AI-assisted ones to rigorously evaluate components like prompts and LLMs. SF Eval addresses challenges in ensuring the relevance and accuracy of AI outputs, incorporating real-time fact-checking, user feedback for continuous improvement, and context-aware retrieval methods. The tool is utilized through development, benchmarking, and production phases to enhance reliability and performance of AI applications.

The Future of AI Testing: Salesforce's New AI Model Performance Framework