Generative AI for R&D: Product Research & Development

Generative AI is rapidly changing how products get researched, designed, and shipped. Consider a few signals: 97% of business leaders say generative AI will be transformative. In drug development, teams have reported cutting early timelines from nearly a decade down to ~36 months using AI-assisted discovery (a ~70% reduction). And in consumer goods, AI-driven experimentation has helped teams cut product development cycles in half.But most teams still struggle to turn “cool demos” into repeatable R&D outcomes. They don’t know which model to use (LLMs vs GANs vs diffusion), how to generate usable synthetic data, how to pressure-test AI ideas, or how to integrate AI into real workflows without introducing hallucinations, privacy leaks, bias, or IP risk.That’s exactly what this course is designed to solve.In this course, you’ll learn how to:Understand generative AI fundamentals for R&D (generative vs discriminative models)Choose the right model type for the job: LLMs, GANs, VAEs, and diffusion modelsUse the most common tools for text, image, code, and domain-specific product developmentGenerate and validate synthetic data to augment scarce, sensitive, or imbalanced datasetsAccelerate literature reviews and knowledge synthesis while avoiding made-up citationsUse AI for hypothesis generation, ideation, trend scanning, and “white space” discoveryPrototype faster with AI-assisted concept generation and generative engineering designApply AI to optimization and scenario testing (constraints, trade-offs, simulations)Write better prompts and use iterative workflows (including structured frameworks)Integrate AI into real R&D workflows using a crawl–walk–run rollout and human reviewManage the real risks: hallucinations, bias, privacy/security, and intell