How this PM streamlines 60k-page FDA submissions and saves millions with Claude, Streamlit, and clever AI workflows | Prerna Kaul
Prerna Kaul is a product and platform leader who has spent over 14 years turning machine-learning research into consumer and B2B products at Amazon Alexa, AGI, Moderna, and now Panasonic Well. In today’s episode, she explains how she’s using AI to slash some of the most time-consuming, expensive tasks in life sciences—from generating 60,000-page FDA submissions to crafting communication frameworks that help product managers navigate complex stakeholder dynamics. Her innovations are saving millions of dollars and helping lifesaving treatments reach the market faster.What you’ll learn:How Prerna built an AI system that automates the creation of 60,000-page regulatory documents for the FDA—reducing a process that took 4 to 6 months and 20 specialists to just minutesA step-by-step system for detecting and redacting PHI (protected health information) in clinical trial data using ClaudeHow to build user-friendly interfaces for non-technical colleagues using Streamlit to democratize AI toolsHow to use Claude’s prompt generator to create powerful communication frameworks that help PMs navigate complex stakeholder situationsWhy transparency about AI costs is crucial for gaining organizational buy-in and tracking ROIA practical framework for approaching AI safety and ethics in highly regulated industries—Brought to you by:CodeRabbit—Cut code review time and bugs in half. Instantly: https://lovable.dev/Lovable—Build apps by simply chatting with AI: https://lovable.dev/—Where to find Prerna Kaul:LinkedIn: https://www.linkedin.com/in/prernakkaul/—Where to find Claire Vo:ChatPRD: https://www.chatprd.ai/Website: https://clairevo.com/LinkedIn: https://www.linkedin.com/in/clairevo/X: https://x.com/clairevo—In this episode, we cover:(00:00) Introduction to Prerna(03:01) The FDA submission challenge: 60,000 pages, months of work, millions in costs(05:20) Getting started in Claude: from prompt to production-ready prototype(10:13) How Claude selected the right models for medical entity recognition(12:04) Using Streamlit to create accessible UIs for non-technical users(16:04) Detecting and redacting PHI in unstructured clinical notes(18:44) Generating the Common Technical Document (CTD) for FDA submission(21:54) Tracking and displaying AI operation costs for stakeholder buy-in(24:38) Real-world impact on vaccine development timelines and costs(26:12) Creating an AI communication coach for product managers(30:22) Training Claude on classic literature and persuasion techniques(31:53) Analyzing a complex stakeholder scenario with multiple competing priorities(34:40) Getting personalized communication strategies inspired by tech leaders(35:40) Summarizing strategic approaches(38:26) Conclusion and final thoughts—Tools referenced:• Claude: https://claude.ai/• Streamlit: https://streamlit.io/• Anthropic Console: https://console.anthropic.com/• Claude Sonnet 4: https://www.anthropic.com/claude/sonnet—Other references:• Claude project chat (AI Product Management Stakeholder Challenges): https://claude.ai/share/caba4ab0-b28a-480c-8633-71920b12999e• XML: https://www.w3.org/XML/• Python: https://www.python.org/• RegEx: https://regex101.com/• Moderna: https://www.modernatx.com/• FDA: https://www.fda.gov/• Project Gutenberg: https://www.gutenberg.org/• FDA Biologics License Application: https://www.fda.gov/vaccines-blood-biologics/development-approval-process-cber/biologics-license-applications-bla-process-cber• Protected health information (PHI): https://www.hhs.gov/hipaa/for-professionals/privacy/laws-regulations/index.html—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email
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