Sanjana

Clinical trial protocol design sits at the intersection of two compounding failure modes in AI systems: hallucination risk (protocols citing nonexistent dosage studies) and regulatory exposure (protocols that quietly omit informed consent clauses or control arms). A system that gets either wrong is not a productivity tool - it is a liability. This post walks through the full reference architecture I built for the Clinical Trial Protocol Architect (CTPA): a production-grade, multi-agent agentic system that uses NVIDIA NIM inference microservices, NeMo Guardrails Colang flows, a three-stage Hybrid RAG pipeline, and RAGAS-gated CI/CD to produce statistically rigorous, ethically compliant clinical trial protocols from a single natural-language prompt. I cover every design decision - model selection, retrieval strategy, parallelization, safety enforcement, and observability - so that the solution can be adapted to any regulated vertical. ...

In the evolution of Generative AI from reactive assistants to proactive agents, a significant hurdle remains: the Inquiry Gap. This metric represents the measurable delta between how a Large Language Model (LLM) currently seeks information and how a Game-Theory Optimal (GTO) oracle would act to minimize uncertainty.1 While current LLMs excel at “System 1” heuristic processing, they often collapse in “System 2” tasks- such as medical diagnostics or complex troubleshooting - where success depends on asking the right sequence of questions to resolve ambiguity. ...

A lot happened in 2025. To summarize, it was the year of “showing up”. I didn’t start the year with a rigid master plan, but by remaining consistent, I reached heights - both literal and figurative, that I hadn’t imagined back in January. Table of Contents Professional: From Small Data to Big Stages Physical: 14k Feet of Grit The Duolingo Discipline Personal: Change and Constancy Social: Breaking the Solitude The Road to Independence Closing Thoughts ...