Evaluating Generative AI for Your Application

Adding generative AI to your software can seem like an obvious step, but without a clear evaluation strategy, it can lead to high costs and poor user experiences. This course helps you cut through the hype and systematically determine if generative AI is the right fit for your application. You will transition from feeling overwhelmed by AI trends to confidently analyzing the technical, financial, and user experience feasibility of AI integration. Through written explanations and practical evaluation scenarios, you will learn to map business problems to AI capabilities and build a solid integration plan. What you'll learn: - Understand foundational generative AI terminology, including Large Language Models (LLMs), prompt engineering, and Retrieval-Augmented Generation (RAG). - Evaluate your data readiness, privacy requirements, and security compliance constraints. - Calculate the total cost of ownership, comparing API-based models to open-source self-hosted alternatives. - Design user experiences that gracefully handle AI hallucinations, errors, and latency. - Analyze real-world use cases to determine when traditional software engineering is better than AI. - Create a structured assessment framework to pitch your AI integration strategy to stakeholders. This course begins with essential AI concepts and vocabulary, then guides you through data, cost, and user experience assessments, concluding with practical written exercises to evaluate your own product ideas. It is designed for software developers, product managers, and technology leaders who are new to generative AI and want to make informed, objective decisions. No prior AI experience is required. Start reading today to make smart, cost-effective decisions for your application's future.