Course Catalog  > Data Science, AI, and ML
B-APPS-LLM

Building Applications with Large Language Models Training

In this Large Language Models (LLMs) course, participants learn how to build practical, innovative, and impactful applications using LMMs like ChatGPT. The course covers model selection, API integration, and prompt engineering. Participants also explore techniques for optimizing AI-generated content to ensure content safety and address biases in AI-driven applications.

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Through hands-on exercises, case studies, and real-world examples, participants gain real-world experience in developing applications that harness the power of state-of-the-art AI technology.

Course Details

Duration

2 days

Prerequisites

  • Strong understanding of AI and machine learning concepts
  • Proficiency in Python programming
  • Familiarity with natural language processing (NLP) techniques and tools is recommended but not mandatory

Target Audience

  • Python Developers
  • Software Engineers
  • AI/ML Engineers
  • Professionals interested in building applications using large language models like ChatGPT

Skills Gained

  • Understand the principles and applications of large language models like ChatGPT in building AI-driven applications
  • Select and integrate large language models into Python applications using APIs
  • Design and refine prompts for AI-generated content tailored to specific use cases
  • Optimize AI-driven applications for performance, content safety, and fairness
  • Develop practical, real-world applications using large language models like ChatGPT
Course Outline
  • Day 1
    • Module 1: Introduction to Large Language Models and Python Integration
      • Overview of large language models (e.g., GPT-3, ChatGPT)
      • Principles of integrating large language models into Python applications
      • Introduction to available APIs and libraries for working with large language models
    • Module 2: Model Selection and API Integration
      • Criteria for selecting appropriate models and architectures for specific use cases
      • Best practices for API integration and handling rate limits
      • Hands-on exercise: Integrating ChatGPT or a similar large language model into a Python application
    • Module 3: Prompt Engineering for AI-Driven Applications
      • Principles of prompt design for AI-generated content
      • Techniques for creating and refining prompts for various use cases
      • Hands-on exercise: Designing and refining prompts for a specific AI-driven application
  • Day 2
    • Module 4: Optimizing AI-Driven Applications for Performance and Safety
      • Techniques for improving AI-generated content quality, relevance, and novelty
      • Ensuring content safety and adherence to ethical guidelines
      • Hands-on exercise: Optimizing an AI-driven application for performance and safety
    • Module 5: Addressing Biases and Fairness in AI-Driven Applications
      • Identifying and mitigating biases in AI-generated content
      • Best practices for promoting fairness and inclusivity in AI-driven applications
      • Hands-on exercise: Evaluating and improving an AI-driven application for fairness
    • Module 6: Capstone Project
      • Participants will apply the concepts and techniques learned throughout the course to build a custom AI-driven application using a large language model like ChatGPT
      • Presentation and discussion of capstone projects