T81 559: Applications of Generative Artificial Intelligence

Washington University in St. Louis

Instructor: Jeff Heaton

• Section 1. Spring 2026, Wednesday, 6:00 PM
  Location: CUPPLES II, Room 00203

Course Description

This course covers the dynamic world of Generative Artificial Intelligence, providing hands-on practical applications of Large Language Models (LLMs) and advanced text-to-image networks. Using Python as the primary tool, students will interact with OpenAI's text and image models. The course begins with a solid foundation in generative AI principles, then moves swiftly to using LangChain for model-agnostic access and the management of prompts, indexes, chains, and agents. A significant focus is on integrating the Retrieval-Augmented Generation (RAG) model with graph databases, unlocking new possibilities for AI applications.

As the course progresses, students will delve into sophisticated image generation and augmentation techniques, including LoRA (Low-Rank Adaptation), and learn the art of fine-tuning generative neural networks for specific needs. The final part of the course is dedicated to mastering prompt engineering, a critical skill for optimizing the efficiency and creativity of AI outputs.

Note: This course will require the purchase of up to $100 in OpenAI API credits.

Objectives

1. Learn how Generative AI fits into the landscape of deep learning and predictive AI.
2. Be able to create ChatBots, Agents, and other LLM-based automation assistants.
3. Understand how to make use of image generative AI programmatically.

Syllabus

This syllabus presents the expected class schedule, due dates, and reading assignments.

Module	Content
Module 1 Meet on 01/12/2026	Module 1: Introduction to Generative AI 1.1 Course Overview 1.2 Generative AI Overview 1.3 Introduction to OpenAI 1.4 Introduction to LangChain 1.5 Prompt Engineering We will meet on campus this week (in-class meeting #1)
Module 2 Week of 01/19/2026	Module 2: Prompt-Based Development 2.1 Prompting for Code Generation 2.2 Handling Revision Prompts 2.3 Using an LLM to Help Debug 2.4 Tracking Prompts in Software Development 2.5 Limits of LLM Code Generation Module 1 Program due: 01/21/2026 Icebreaker due: 01/21/2026
Module 3 Week of 01/26/2026	Module 3: Introduction to Large Language Models 3.1 Foundation Models 3.2 Text Generation 3.3 Text Summarization 3.4 Text Classification 3.5 LLM Writes a Book Module 2 Program due: 01/27/2026
Module 4 Meet on 02/02/2026	Module 4: LangChain — Chat and Memory 4.1 LangChain Conversations 4.2 Conversation Buffer Window Memory 4.3 Chat with Summary and Fixed Window 4.4 Chat with Persistence, Rollback and Regeneration 4.5 Automated Coder Application Module 3 Program due: 02/03/2026 We will meet on campus this week (in-class meeting #2)
Module 5 Week of 02/09/2026	Module 5: LangChain — Data Extraction 5.1 Structured Output Parser 5.2 Other Parsers (CSV, JSON, Pandas, Datetime) 5.3 Pydantic Parser 5.4 Custom Output Parser 5.5 Output-Fixing Parser Module 4 Program due: 02/10/2026
Module 6 Meet on 02/16/2026	Module 6: Retrieval-Augmented Generation (RAG) 6.1 Introduction to RAG 6.2 Introduction to ChromaDB 6.3 Understanding Embeddings 6.4 Q&A Over Documents 6.5 Embedding Databases Module 5 Program due: 02/17/2026 We will meet on campus this week (in-class meeting #3)
Module 7 Week of 02/23/2026	Module 7: LangChain — Agents 7.1 Introduction to LangChain Agents 7.2 Understanding LangChain Agent Tools 7.3 LangChain Retrieval and Search Tools 7.4 Constructing LangChain Agents 7.5 Custom Agents Module 6 Program due: 02/24/2026
Module 8 Meet on 03/02/2026	Module 8: Kaggle Assignment 8.1 Introduction to Kaggle 8.2 Kaggle Notebooks 8.3 Small Large Language Models 8.4 Accessing Small LLMs from Kaggle 8.5 Current Semester’s Kaggle Module 7 Program due: 03/03/2026 We will meet on campus this week (in-class meeting #4)
Module 9 Week of 03/16/2026	Module 9: Multimodal and Text-to-Image 9.1 Introduction to Multimodal and Text-to-Image 9.2 Generating Images with DALL·E 9.3 Editing Existing Images with DALL·E 9.4 Multimodal Models 9.5 Illustrated Book Module 8 Program due: 03/17/2026
Module 10 Week of 03/23/2026	Module 10: Introduction to Streamlit 10.1 Running Streamlit in Google Colab 10.2 Streamlit Introduction 10.3 Understanding Streamlit State 10.4 Creating a Chat Application 10.5 More Advanced Chat Application Module 9 Program due: 03/24/2026
Module 11 Week of 03/30/2026	Module 11: Fine Tuning 11.1 When is Fine Tuning Necessary 11.2 Preparing a Dataset for Fine Tuning 11.3 OpenAI Fine Tuning 11.4 Application of Fine Tuning 11.5 Evaluating Fine Tuning and Optimization Module 10 Program due: 03/31/2026
Module 12 Week of 04/06/2026	Module 12: Prompt Engineering 12.1 Intro to Prompt Engineering 12.2 Few-Shot and Chain-of-Thought 12.3 Persona and Role Patterns 12.4 Question, Refinement, and Verification Patterns 12.5 Content Creation and Structured Prompt Patterns
Module 13 Week of 04/13/2026	Module 13: Speech Processing 13.1 Voice-Based ChatBots 13.2 OpenAI Speech Generation 13.3 OpenAI Speech Recognition 13.4 A Voice-Based ChatBot 13.5 Future Directions in GenAI Kaggle Competition Closes: 4/19/2026 (midnight), assignment due in Canvas 04/21/2026
Week 14 Week of 04/20/2026	Wrapup Discuss final Kaggle results and future directions of this technology.