Generative AI in Research and Development

Research and development landscapes are transforming rapidly as generative artificial intelligence introduces new ways to analyze data, synthesize information, and accelerate discovery. This course provides a clear, text-based path to understanding how generative AI can be practically applied to modern R&D workflows. By the end of this course, you will transition from understanding basic AI concepts to actively identifying opportunities where generative models can enhance research efficiency, analyze complex data patterns, and support decision-making processes. What you'll learn: - Understand foundational generative AI concepts, models, and terminology essential for research environments. - Apply basic prompt engineering techniques to extract high-quality, relevant information from AI models. - Analyze data-driven R&D processes to identify where generative tools can streamline workflows. - Explore modern retrieval-augmented generation patterns to ground AI outputs in reliable research data. - Evaluate AI-generated content for accuracy, bias, and hallucination to maintain scientific integrity. - Practice responsible AI guidelines to ensure ethical usage and data privacy in your research. The course begins with essential definitions and core concepts of generative AI before moving into practical methodologies for data analysis, prompt design, and workflow integration. You will read real-world scenarios and complete written exercises designed to build your confidence in deploying AI solutions. This course is designed for beginners, researchers, analysts, and project managers looking to integrate modern AI tools into their R&D work. No prior programming or advanced technical background is required. Start reading today to modernize your research workflows with generative AI.