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app_generative_ai

T81-559: Applications of Generative Artificial Intelligence

https://github.com/jeffheaton/app_generative_ai

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T81-559: Applications of Generative Artificial Intelligence

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  • Host: GitHub
  • Owner: jeffheaton
  • License: apache-2.0
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 7.58 MB
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  • Stars: 159
  • Watchers: 8
  • Forks: 59
  • Open Issues: 0
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Created over 2 years ago · Last pushed 11 months ago
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README.md

T81 559:Applications of Generative Artificial Intelligence

Washington University in St. Louis

Instructor: Jeff Heaton

  • Section 1. Fall 2025, 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 models for both text and images. The course begins with a solid foundation in generative AI principles, moving swiftly into the utilization of LangChain for model-agnostic access and the management of prompts, indexes, chains, and agents. A significant focus is placed on the integration of the Retrieval-Augmented Generation (RAG) model with graph databases, unlocking new possibilities in 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. Ideal for students, researchers, and professionals in computer science or related fields, this course offers a transformative learning experience where technology meets creativity, paving the way for innovative applications in the realm of Generative AI.

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

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 programatically.

Syllabus

This syllabus presents the expected class schedule, due dates, and reading assignments. Download current syllabus. | Module | Content | | ---------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Module 1
Meet on 08/26/2025 | 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! (first meeting)
| | Module 2
Week of 09/02/2025 | Module 2: Prompt Based Development
  • 2.1: Prompting for Code Generation
  • 2.2: Handling Revision Prompts
  • 2.3: Using a LLM to Help Debug
  • 2.4: Tracking Prompts in Software Development
  • 2.5: Limits of LLM Code Generation
  • Module 1 Program due: 09/03/2025
  • Icebreaker due: 09/03/2025
| | Module 3
Week of 09/09/2025 | 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: 09/10/2025
| | Module 4
Week of 09/16/2025 | Module 4: LangChain: Chat and Memory
  • Part 4.1: LangChain Conversations
  • Part 4.2: Conversation Buffer Window Memory
  • Part 4.3: Chat with Summary and Fixed Window
  • Part 4.4: Chat with Persistence, Rollback and Regeneration
  • Part 4.5: Automated Coder Application
  • Module 3: Program due: 09/17/2025
| | Module 5
Meet on 09/23/2025 | 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: 09/24/2025
  • We will meet on campus this week! (second meeting) | | Module 6
    Week of 09/30/2025 | 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: 10/01/2025
    | | Module 7
    Week of 10/14/2025 | Module 7: LangChain: Agents
    • 7.1: Introduction to LangChain Agents
    • 7.2: Understanding LangChain Agent Tools
    • 7.3: LangChain Retrival and Search Tools
    • 7.4: Constructing LangChain Agents
    • 7.5: Custom Agents
    • Module 6 Program due: 10/15/2025
    | | Module 8
    Meet on 10/21/2025 | Module 8: Kaggle Assignment
    • 8.1: Introduction to Kaggle
    • 8.2: Kaggle Notebooks
    • 8.3: Small Large Language Models
    • 8.4: Accessing Small LLM from Kaggle
    • 8.5: Current Semester's Kaggle
    • Module 7 Program due: 10/22/2025
    • We will meet on campus this week! (third meeting)
    | | Module 9
    Week of 10/28/2025 | Module 9: MultiModal and Text to Image
    • 9.1: Introduction to MultiModal and Text to Image
    • 9.2: Generating Images with DALLE
    • 9.3: Editing Existing Images with DALLE
    • 9.4: MultiModal Models
    • 9.5: Illustrated Book
    • Module 8 Program due: 10/29/2025
    | | Module 10
    Week of 11/04/2025 | 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: 11/05/2025
    | | Module 11
    Week of 11/11/2025 | Module 11: Fine Tuning
    • 11.1: When is fine tuning necessary
    • 11.2: Preparing a dataset for fine tuning
    • 11.3: OepnAI Fine Tuning
    • 11.4: Application of Fine Tuning
    • 11.5: Evaluating Fine Tuning and Optimization
    • Module 10 Program due: 11/12/2025
    | | Module 12
    Week of 11/18/2025 | 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 11/25/2025 | 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 Assignment due: 11/26/2025 (midnight)
    | | Week 14
    Meet on 12/02/2025 | Week 14: Kaggle Presentations
    • Top Kaggle teams will present
    • We will meet on campus this week! (fourth meeting)
    • Final project due: 12/02/2025
    |

    • Owner

    • Name: Jeff Heaton
    • Login: jeffheaton
    • Kind: user
    • Location: St. Louis, MO, USA
    • Company: Reinsurance Group of America (@rgare)

    Computer scientist that specializes in data science and artificial intelligence. VP AI Innovation at RGA, Adjunct faculty at WashU.

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    Last synced: about 1 year ago

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    Heaton j****n@l****t 3
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