Augmenting LLM Prompts for Contextual Clarity: Building a Retrieval Augmented Generation (RAG) System using Gemma & MongoDB

Ashmi Banerjee (~ashmi)




Join us for an insightful conversation on leveraging a RAG (Retrieval-Augmented Generation) system with Gemma and MongoDB. RAG serves as an AI framework, grounding large language models (LLMs) with accurate, up-to-date information retrieved from external knowledge bases, enhancing users' understanding of LLMs' generative process.

In the initial segment of our session, we'll explore the process of accessing Google's Gemma-2b-it model via HuggingFace's inference client, enabling us to generate text seamlessly. Following this, we'll delve into the augmentation of our travel queries for the Large Language Model (LLM) by incorporating relevant content from a vector database created using MongoDB. Our database will leverage Wikipedia data, focusing solely on abstracts, for 160 European cities. Additionally, we'll discover how to deploy our application effortlessly on Gradio—an open-source Python package that simplifies building demos or web applications without requiring any JavaScript, CSS, or web hosting expertise.


  • Basic understanding of machine learning concepts- LLMs, Recommender Systems
  • Familiarity with Python programming language
  • Curiosity & willingness to engage in hands-on exploration

Video URL:

Speaker Info:

Ashmi is currently a doctoral researcher at the Technical University of Munich. Her research focuses on Recommender Systems and Human-Computer Interaction. She graduated with a master's degree in Computer Science in 2019 from the same university and also holds three years of industry experience at different companies across Germany.

She is passionate about using technology to automate tedious tasks and is always excited to tackle new technical challenges. She has been a Google Developer Expert (GDE) in Machine Learning since 2023.

Ashmi was named one of the 100 technologists to watch for 2023 and won the Google Developer Expert Community Award (Rising Star), the Women Who Code Applaud Her Award (Data Science, 2023), and the DevelopHER Awards 2022 (Emerging Talent).

As a Google Women Techmakers (WTM) Ambassador diversity advocate, she is dedicated to closing the gender gap in STEM through her involvement in various women in STEM networks.

She travels or trains as a triathlete when not sitting in front of her computer. 🏊‍♀️ 🚴 🏃‍♀️

Speaker Links:


Speaking Engagements

Recorded Talks

Youtube Playlist

Personal Website

Section: Artificial Intelligence and Machine Learning
Type: Talk
Target Audience: Intermediate
Last Updated: