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Learn to build and iterate on generative AI-powered applications through this comprehensive workshop that bridges software engineering and AI development. Explore the complete AI software development lifecycle using first principles thinking, covering essential components like prompt engineering, monitoring, evaluations, and handling non-determinism in AI systems. Master techniques for integrating AI models and APIs into practical applications while managing the unique engineering challenges of AI-powered systems. Discover how to use multimodal AI models to build applications such as PDF-querying tools, with all techniques being generalizable for various generative AI applications. Gain hands-on experience in monitoring, logging, and evaluating AI systems to ensure reliability, while learning to handle structured outputs and implement function calling in AI models. Understand the critical software engineering aspects of AI applications including lifecycle management, iterative development, debugging, and performance monitoring for production-level systems. Build practical skills using vanilla Python and direct LLM calls rather than relying solely on frameworks, emphasizing fundamental understanding over tool-specific knowledge. Suitable for software engineers, data scientists, machine learning practitioners, and AI enthusiasts with basic Python programming knowledge and familiarity with REST APIs, though no prior AI or machine learning experience is required.
