RAG Explained: The Architecture Behind Agentic AI Systems

Hosted by Aishwarya Srinivasan and Arvind Narayan

Sat, Apr 4, 2026

3:00 PM UTC (30 minutes)

Virtual (Zoom)

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117 students

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Building a career in AI as a non-coder
Aishwarya Srinivasan
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What you'll learn

Understand the fundamentals of RAG

Learn the core RAG architecture, why it matters, and how retrieval improves LLM reliability.

Learn the key RAG patterns used in modern AI applications

Understand common RAG patterns and when to use them in copilots, assistants, and agent systems.

Understand how RAG systems are engineered at scale

Learn how teams design production RAG systems including evaluation, latency, and reliability.

Why this topic matters

RAG has become the foundation of most real AI applications today. Instead of relying only on a model’s training data, modern systems retrieve relevant information from documents, databases, and company knowledge. Understanding RAG helps you design reliable AI assistants, copilots, and agentic systems that actually work in real organizational environments.

You'll learn from

Aishwarya Srinivasan

AI Entrepreneur | Ex-Google, Microsoft

Most Followed Indian Woman in AI | Founder @ The Gen Academy | AI Leader | Ex - Fireworks AI, Google, Microsoft, IBM

Aishwarya has built one of the most trusted AI communities in the world with over 1 Million followers who rely on her to cut through the nice.

She spent a decade building AI at the companies that defined the modern stack Google, Microsoft, and IBM. As the head of AI Developer Relations at Fireworks AI, she worked directly at the interface businesses, builders and the infrastructure powering production-scale AI systems. She holds a Master's in Data Science from Columbia University.

Aishwarya's teaching philosophy is simple: explain the real thing, not the sanitised version. Her sessions are known for being brutally honest, technically sharp, and remarkably clear.

Arvind Narayan

AI Engineering Lead | Founder

Founder @ The Gen Academy | AI Solutions Architect | Ex - Adobe, Microsoft, IBM

Arvind brings a rare cross-company perspective on AI at scale, built across Microsoft, IBM, and Adobe — where he led data science teams and developed production-grade ML solutions tied to significant ARR growth.

He currently serves as AI Solutions Architect at Ema, a leading AI startup focused on agentic enterprise transformation, and is Founder of Eikos Health, an agentic scheduling platform for doctor-led clinics.

Arvind holds a Master's from Carnegie Mellon University. His teaching style emphasizes practical thinking, strong mental models, and translating AI concepts into production-ready solutions.

Previously at

Google
Microsoft
Adobe
Columbia University
Carnegie Mellon University

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