2025-11-09 –, Tutorial Track 1
In this hands-on tutorial, we’ll walk through building a lightweight, agent-style workflow that takes a user-specified topic and uses retrieval-augmented generation (RAG) to perform deep research, summarize insights, and generate a podcast-style script. We’ll also show how to convert that script into audio using a simple text-to-speech tool.
This is a beginner-friendly, practical workshop that introduces key concepts in agent task design and content orchestration using LLMs.
This tutorial introduces a practical, framework-agnostic approach to building agent-like workflows using RAG and LLMs. Attendees will create a working prototype that:
Accepts a user-provided topic
Performs retrieval-augmented generation using a pre-indexed corpus or API-accessible data
Synthesizes key points into structured research insights
Generates a conversational, podcast-style script
Converts the script into audio output using a simple TTS tool
The goal is to help participants think in terms of agent roles, decompose tasks, and orchestrate multi-step LLM workflows — all without needing advanced infrastructure or orchestration frameworks.
What You'll Learn
- How to use RAG for deep topic exploration from a simple user input
- Designing basic agent-style task flows: “Researcher,” “Summarizer,” and “Host”
- Prompt engineering techniques for structured outputs and dialogue format
- Tools for converting text to audio, including ElevenLabs
No previous knowledge expected
ML Engineer at Walmart
I’m currently a full-stack machine learning engineer at Walmart E-commerce, where I get to tackle exciting challenges in the world of online retail. Before that, I was a data scientist at Bank of America, building real-time fraud detection models using deep neural networks and big data – talk about high stakes!
My research interests lie in the fascinating areas of graph embedding, neural architecture search, and fast optimization methods for neural networks. I love pushing the boundaries of what’s possible with AI.
But my passion for technology extends beyond my day job. I’m also deeply invested in two side projects:
AI-Powered Vision for IoT: I’m exploring the potential of NVidia Jetson Nano to create innovative machine learning vision applications for the Internet of Things.
ML Design Patterns: I’m developing reusable design patterns to solve common machine learning problems, making AI development more efficient and accessible.
And when I need a break from the digital world, I head to my garden. I’m an avid grower of Cayenne peppers – the hotter, the better!
My journey to AI was paved with diverse experiences. Earlier in my career, I worked on NLP-based automated evaluation of text data, gaining valuable insights into the power of language processing. I hold a master’s degree in computer science from North Carolina State University – Raleigh (graduated in Spring 2016) and a bachelor’s degree in electronics and communication engineering.