---
title: 'Faculty Seminar: Dr. Md. Ariful Haque'
url: 'https://eee.buet.ac.bd/blog/20241205-faculty-seminar-dr-ariful%20haque'
markdown: 'https://eee.buet.ac.bd/blog/20241205-faculty-seminar-dr-ariful%20haque.md'
date: '2024-12-11'
description: 'Speaker: Dr.Mohammmad Ariful Haque, Professor, EEE, BUET Title of the Talk: Transformers, LLMs, and RAG: Introduction to Building a Personal Research Assistant with Generative AI Date: 11 December 2024 Time: 3 PM Venue: Room number 920, ECE building, West Palashi, BUET, Dhaka-1000 Abstract: Ge…'
taxonomy:
  category:
    - blog
  tag:
    - facultyseminar
    - faculty
    - seminar
  archives_month:
    - dec_2024
  archives_year:
    - '2024'
---

# Faculty Seminar: Dr. Md. Ariful Haque

[ Home ](https://eee.buet.ac.bd/)      Faculty Seminar: Dr. Md. Ariful Haque    

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**Speaker:** Dr.Mohammmad Ariful Haque, Professor, EEE, BUET

**Title of the Talk:** Transformers, LLMs, and RAG: Introduction to Building a Personal Research Assistant with Generative AI

**Date:** 11 December 2024

**Time:** 3 PM

**Venue:** Room number 920, ECE building, West Palashi, BUET, Dhaka-1000

**Abstract:**

Generative AI is transforming the way we work, learn, and solve problems. In electrical and electronic engineering, it has applications in areas such as automated system design, power system optimization, smart grids, predictive maintenance, fault detection and diagnosis, signal processing, robotics, creating virtual labs and research assistants. At the core of generative AI is the transformer architecture, a powerful tool that enables artificial neural networks to process and generate human-like text and other data.

This talk introduces the foundational concepts of transformers and explains how they are used to build large language models (LLMs) like GPT, ChatGPT, BERT, T5, and LLAMA. We will discuss how these models learn from data using self-supervised learning, how they can be customized through fine-tuning, and how reinforcement learning with human feedback (RLHF) improves their performance.

To make AI systems more reliable, we will explore retrieval-augmented generation (RAG), a method that connects LLMs to external information sources to reduce errors and ground their responses in factual data. The talk includes a live demonstration of building a research assistant using the RAG pipeline. We will also discuss the future of AI, including multimodal models that handle text, images, and more, and introduce NotebookLM, an advanced AI-powered research assistant developed by Google.

This session aims to inspire students and faculty members to explore the potential of generative AI in their academic and professional endeavors.

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