Developing with AI and AUTO-GPT

An intermediate-level course helping students gain technical expertise in development with AI technologies and Auto-GPT. The course also covers Langchain and its integration with AI technologies.

Course Outline

Unit 1: Introduction to AI Development

• 1.1 Review of AI and Machine Learning concepts
• 1.2 The role of a developer in AI projects
• 1.3 The AI development process
• 1.4 Overview of AI development tools and libraries

Unit 2: Deep Dive into GPT Models

• 2.1 Understanding transformer architectures
• 2.2 The mechanics of GPT models
• 2.3 The role of attention in GPT models
• 2.4 Use-cases and applications of GPT models

Unit 3: Introduction to AUTO-GPT

• 3.1 Understanding AUTO-GPT
• 3.2 AUTO-GPT vs. traditional GPT models
• 3.3 Building applications with AUTO-GPT
• 3.4 Handling challenges and limitations of AUTO-GPT

Unit 4: Langchain in AI Development

• 4.1 Introduction to Langchain Python library
• 4.2 Integrating Langchain with GPT and AUTO-GPT models
• 4.3 Use-cases and applications of Langchain
• 4.4 Best practices when using Langchain

Unit 5: Practical AI Development

• 5.1 Building a simple application using GPT and AUTO-GPT
• 5.2 Exploring different use-cases: text generation, question answering, and more
• 5.3 Debugging and optimizing your AI applications
• 5.4 Ensuring the reliability and robustness of AI applications

Unit 6: Prompt Engineering for GPT and AUTO-GPT

• 6.1 Understanding the importance of prompt engineering
• 6.2 Techniques for effective prompt design
• 6.3 Mitigating model biases through prompt engineering
• 6.4 Practical exercises in prompt engineering

Unit 7: Introduction to AI Ethics and Fairness

• 7.1 Understanding bias and fairness in AI
• 7.2 Techniques for ensuring fairness in AI applications
• 7.3 Ethical considerations in AI development

Unit 8: Deploying AI Applications

• 8.1 Understanding the deployment process for AI applications
• 8.2 Challenges in deploying AI applications
• 8.3 Best practices for deployment and maintenance

Intended Audience

The intended audience for this course is software developers and engineers seeking to build expertise in applied AI development with generative models like GPT and AUTO-GPT. Ideal students will have 1-3 years of professional coding experience and background knowledge of major AI concepts, but want to expand their skills into leveraging state-of-the-art natural language models to create real-world applications. This course aims to provide the practical techniques needed to prototype and deploy AI systems integrating tools like Langchain and AUTO-GPT responsibly and effectively.

Prerequisites

Those attending this course should meet the following:

• Basic programming experience (e.g. Python, JavaScript, Java)
• Familiarity with machine learning concepts and techniques
• Experience with data analysis and manipulation
• Knowledge of REST APIs and ability to integrate different services/tools
• Basic understanding of natural language processing
• Experience with Git and version control systems
• Understanding of cloud platforms like GCP, AWS, or Azure