Developing Generative AI Solutions

Generative AI is transforming how we build software and solve business problems, but moving from a simple prompt to a production-ready application requires a structured approach. This course provides a clear roadmap for developing AI-driven solutions that are reliable, scalable, and aligned with organizational goals. You will gain a comprehensive understanding of the generative AI development lifecycle, learning how to transition from initial concept to a deployed solution. By the end of this course, you will be able to strategically select the right tools and techniques to build intelligent applications that deliver real-world impact. What you'll learn: - Define business use cases that are best suited for generative AI technologies. - Select foundation models based on specific performance, cost, and latency requirements. - Improve model accuracy using prompt engineering and Retrieval-Augmented Generation (RAG). - Understand the role of vector databases in managing and retrieving proprietary data. - Evaluate model performance using both automated metrics and human-centric feedback. - Deploy AI solutions while considering operational impact and business objectives. - Apply basic safety and ethical guardrails to generative AI outputs. The course begins with essential terminology and foundational concepts before walking through the strategic stages of model selection, customization, and deployment. You will explore modern patterns like RAG and observability to ensure your AI solutions remain effective over time. This course is designed for beginners, aspiring AI developers, and project managers who want to understand the technical and strategic side of AI development. No prior experience with machine learning or artificial intelligence is required. Begin building your expertise in the rapidly evolving field of generative AI development today.