Harnessing AI APIs for Safer, Accurate, and Reliable Applications

Explore strategies for harnessing AI APIs to build safer, more accurate, and reliable applications in this podcast episode featuring Ron Heichman, Machine Learning Engineer at SentinelOne. Delve into practical approaches for integrating AI APIs in production environments, focusing on adapting them to specific use cases, mitigating risks, and enhancing performance. Learn about testing, measuring, and improving quality for Retrieval-Augmented Generation (RAG) and AI-assisted knowledge work. Gain insights into AI model evolution, challenges in AI tool accessibility, optimizing LLM performance, red teaming taxonomy, and securing custom LLMs. Discover the importance of diverse data in LLMs, automated data diversity feedback, and model stress-testing processes. Examine prompt injection patterns, notable jailbreak attempts, and data poisoning vulnerabilities to better understand and address potential security risks in AI systems.

[] Ron's preferred coffee
[] Takeaways
[] Register now for the Data Engineering for AIML Conference!
[] AI vs ML Solutions
[] AI Application challenges
[] AI Model evolution
[] AI tools accessibility challenge
[] AI tools accessibility gap
[] Optimizing LLM Performance
[] Red teaming taxonomy
[] Securing custom LLMs
[] Diverse data in LLMs
[] Automated data diversity feedback
[] Model stress-testing process
[] Early issue detection benefits
[] Prompt injection patterns
[] Best jailbreaks seen by Ron
[] Data poisoning vulnerabilities
[] Wrap up