When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
Building Applications with Large Language Models
In this 2-day course, participants will learn how to build practical applications using large language models like ChatGPT. The course will cover the essentials of working with large language models,...
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$2,000 USD
Course Code
B-APPS-LLM
Duration
2 days
Available Formats
Classroom
In this 2-day course, participants will learn how to build practical applications using large language models like ChatGPT. The course will cover the essentials of working with large language models, including model selection, API integration, and prompt engineering. Participants will also explore techniques for optimizing AI-generated content, ensuring content safety, and addressing biases in AI-driven applications. Through hands-on exercises, case studies, and real-world examples, participants will gain practical experience in developing applications that harness the power of state-of-the-art AI technology. By the end of the course, participants will have the skills and knowledge required to create innovative and impactful AI-driven solutions 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
Who Can Benefit
Python developers, software engineers, AI/ML engineers, and professionals interested in building applications using large language models like ChatGPT
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
Course Details
Throughout the course, participants will engage in hands-on exercises, case studies, and group discussions to reinforce learning and encourage collaboration among peers. The capstone project at the end of the course will provide an opportunity for participants to showcase their skills by developing a custom AI-driven application using a large language.
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