Pioneering Generative AI in Pharma:

Launching Lilly’s First Patient-Facing LLM Chatbot

Background

Healthcare expectations have fundamentally shifted. Patients accustomed to ChatGPT's instant, personalized responses now expect the same from their healthcare interactions. Traditional static content delivery was no longer sufficient — especially in high-demand areas like obesity treatment, where patients and providers needed rapid, conversational access to trusted medical information.

The strategic question: How might we deliver on-demand, conversational access to complex product information using generative AI — while ensuring regulatory compliance and safeguarding patient safety — in a way that positions Lilly as an innovation leader?

My Role

As Strategic Product Lead, I drove the end-to-end effort to bring Lilly's first generative AI chatbot from concept to launch — with no industry precedent to follow.

Key Responsibilities:

  • Problem Definition: Balanced patient experience goals with stringent regulatory and medical risk considerations

  • Cross-functional Leadership: Partnered with engineering, medical, legal, and compliance teams to shape an entirely new approach to conversational AI in pharma

  • Architecture Strategy: Influenced architectural decisions supporting a novel hybrid model for scalable, safe conversational experiences

  • Process Innovation: Designed continuous refinement and risk monitoring processes with embedded governance and feedback loops

  • Executive Alignment: Led stakeholder alignment and championed the product vision across senior leadership

💡 Innovation & Process Design

With no pharma precedent for generative AI deployment, we built foundational processes from the ground up.

Multi-tiered Continuous Refinement

Implemented monitoring and feedback loops featuring:

  • Human-in-the-loop reviews for quality assurance

  • Automated safeguards for early deviation detection

  • Adaptive learning from evolving usage patterns

  • Real-time risk assessment and mitigation

Agile Compliance Framework

Created entirely new review workflows that:

  • Aligned medical, legal, and regulatory teams around iterative approvals

  • Kept pace with AI model learning cycles

  • Departed radically from traditional static content sign-offs

  • Enabled continuous improvement while maintaining safety standards

Hybrid Conversational Architecture

Designed a novel blend of retrieval and generation that:

  • Balanced responsiveness with control

  • Reduced hallucination likelihood

  • Ensured outputs stayed within approved content parameters

  • Enabled scalable yet safe conversational experiences

Results & Validation

Test Scenario: Sales team meeting with 6 stakeholder requests + existing Q2/Q3 roadmap Output Quality:

  • ✅ Captured all explicit requests with supporting quotes

  • ✅ Identified implicit performance and UX needs

  • ✅ Correctly prioritized features based on deal impact

  • ✅ Flagged realistic technical risks for React/Node.js stack

  • ✅ Identified roadmap conflicts (mobile priority mismatch)

Time Savings: Reduces 2-3 hour manual analysis to 5-minute structured output

🔗 Live Demo

Try MeetingToRoadmap →

Built on Amazon Bedrock PartyRock - requires AWS account for access

Future Vision: Multi-App Product Suite

While PartyRock served as an excellent prototyping platform, the real potential lies in orchestrated multi-step workflows:

Planned App Ecosystem:

1. Visual Roadmap Parser

  • Upload roadmap images/PDFs → Extract structured feature data

  • Integration: Feeds into current Meeting Analyzer

2. Competitive Intelligence Analyzer

  • Input: Competitor feature lists, pricing pages

  • Output: Gap analysis vs. your roadmap

3. User Feedback Synthesizer

  • Input: Support tickets, user interviews, NPS comments

  • Output: Prioritized user pain points

4. Technical Feasibility Scorer

  • Input: Feature requirements + current architecture

  • Output: Detailed effort estimates and implementation paths

Technical Implementation for Multi-App Orchestration:

Since PartyRock doesn't support multi-app workflows, I'd build this using:

LangChain/LangGraph Pipeline:

Visual Parser → Feature Extractor → Meeting Analyzer → 
Competitive Intel → Final Roadmap Recommendation

Architecture:

  • Frontend: React dashboard for file uploads and workflow management

  • Backend: Node.js orchestrator calling different LLM models

  • Data Flow: Structured JSON between each processing step

  • Storage: PostgreSQL for feature tracking and historical analysis

🎓 Key Learnings

Prompt Engineering:

  • Specificity in input formats dramatically improves output quality

  • Grounding analysis in multiple contexts (roadmap + tech stack) increases relevance

  • Table structures need explicit formatting instructions for consistency

Platform Limitations:

  • PartyRock excellent for single-step analysis but limited for complex workflows

  • File upload capabilities enable real-world PM workflows

  • Need to balance comprehensive analysis with concise, scannable outputs

Product Insights:

  • Real PM workflows require orchestration between multiple analysis types

  • Context awareness (existing roadmap + tech constraints) is crucial for actionable output

  • Speed vs. depth tradeoff: 5-minute analysis vs. 3-hour manual deep dive

💭 Reflection

This project demonstrated the power of AI for structured analysis while highlighting the need for thoughtful prompt engineering and workflow orchestration. The single-app PartyRock version proves the concept, but the real value lies in building interconnected analysis tools that can handle the full complexity of modern product management workflows