ChatGPT Developer Training

Curriculum:

Module 1: Introduction to ChatGPT and Conversational AI

• Evolution of AI, NLP, and generative models
• Overview of GPT models: GPT-3, GPT-3.5, GPT-4
• ChatGPT capabilities, limitations, and use cases
• Ethical AI and responsible deployment practices

Module 2: Understanding GPT Architecture

• Transformer architecture and attention mechanism
• Tokenization, embeddings, and context management
• Model sizes, parameters, and trade-offs
• Comparing GPT with other AI models (BERT, T5, LLaMA)

Module 3: Working with OpenAI API

• OpenAI API overview and authentication
• Making requests and handling responses
• Rate limits, usage quotas, and cost management
• Hands-on exercises: creating basic ChatGPT responses

Module 4: Prompt Engineering

• Fundamentals of prompt design
• Few-shot, zero-shot, and chain-of-thought prompting
• System vs. user messages and context handling
• Practical exercises with prompt tuning for accuracy

Module 5: Building Conversational Applications

• Designing conversation flows and handling multi-turn dialogues
• Context preservation and state management
• Integrating ChatGPT with web, mobile, and messaging platforms
• Hands-on lab: Chatbot development for customer support

Module 6: Fine-Tuning and Customization

• Introduction to fine-tuning GPT models
• Data preparation and formatting for domain-specific tasks
• Implementing embeddings for semantic search and retrieval-augmented generation (RAG)
• Use cases for industry-specific AI solutions

Module 7: Integration and Automation

• Connecting ChatGPT with APIs, databases, and business workflows
• Building AI agents for automation
• Webhooks, serverless functions, and event-driven architecture
• Hands-on lab: Integrating ChatGPT with Slack, Teams, or web apps

Module 8: Monitoring, Optimization, and Security

• Logging, monitoring, and analyzing AI outputs
• Handling hallucinations and unintended outputs
• Security best practices: API keys, access control, and data privacy
• Cost optimization strategies and performance tuning

Module 9: Advanced Use Cases

• AI-powered content generation and summarization
• Personalized recommendations and customer engagement
• Knowledge management and internal enterprise bots
• Industry-specific use cases: finance, healthcare, e-commerce

Module 10: Capstone Project

• Design and develop a complete conversational AI application
• Integrate multi-turn conversations, APIs, and domain logic
• Implement monitoring, error handling, and performance optimization
• Present project outcomes and best practices