ChatGPT (GPT-4) for Developers

• Proficiency in a programming language (e.g., Python)
• Familiarity with AI and natural language processing concepts

Audience

• Developers
• Software engineers
• AI enthusiasts

ChatGPT (GPT-4) is an advanced AI-based language model developed by OpenAI. It provides developers with powerful natural language processing capabilities for building conversational agents, chatbots, virtual assistants, and more. 

This instructor-led, live training (online or onsite) is aimed at developers who wish to harness the power of ChatGPT (GPT-4) in their applications. Participants will gain practical knowledge and hands-on experience in integrating ChatGPT into their projects.

By the end of this training, participants will be able to:

• Understand the architecture and working principles of ChatGPT.
• Utilize the ChatGPT API to interact with the model programmatically.
• Develop conversational agents and chatbots using ChatGPT.
• Explore new features and functionalities offered by GPT-4 to enhance their applications.
• Customize and fine-tune ChatGPT for specific applications.

Format of the Course

• Interactive lectures and discussions.
• Hands-on exercises and coding sessions.
• Q&A sessions for clarifications and problem-solving.
• Live project implementation and demonstrations.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to ChatGPT

• Overview of ChatGPT and its capabilities
• Understanding the advancements in GPT-4 architecture

Working with ChatGPT API

• Accessing the ChatGPT API
• Making API requests and handling responses
• Exploring API parameters and options

Building Conversational Agents

• Designing the conversational flow of your agent
• Integrating ChatGPT into your application
• Handling user input and generating model responses

Leveraging Enhanced Capabilities of GPT-4

• Harnessing GPT-4's improved performance for natural language understanding and generation
• Utilizing advanced features such as multi-modal learning and context awareness

Customizing ChatGPT

• Fine-tuning ChatGPT for specific domains or tasks
• Dataset preparation for fine-tuning
• Techniques for improving model performance and accuracy

Scaling and Deployment Considerations

• Managing high volumes of user interactions
• Handling concurrency and optimizing performance
• Deploying ChatGPT in production environments

Best Practices and Ethical Considerations

• Ensuring responsible and ethical use of ChatGPT
• Addressing biases and ethical concerns in AI applications
• Keeping up with the latest developments and trends in AI

Future Developments and Trends

• Exploring the future directions of language models beyond GPT-4
• Insights into ongoing research and upcoming advancements in AI

Summary and Next Steps