Generative AI Assistants Specialization Course Syllabus

Overview: This specialization provides a hands-on introduction to building generative AI assistants, covering core components like prompting, memory, tools, and ethics. Over five modules and a final project, learners will gain practical experience designing and deploying intelligent assistants using real-world tools and frameworks. The course is designed for beginners with basic technical skills and can be completed in approximately 5 weeks with a time commitment of 3-5 hours per week.

Module 1: Introduction to Generative AI Assistants

Estimated time: 4 hours

• What are generative AI assistants?
• Key components of assistant architecture
• Real-world examples and use cases
• Understanding context windows and conversation loops

Module 2: Tools, Memory, and Retrieval

Estimated time: 4 hours

• Integrating external tools with AI assistants
• Introduction to vector databases and retrieval-augmented generation (RAG)
• Handling memory in assistant workflows
• Building memory-enhanced responses

Module 3: Prompting and Planning

Estimated time: 4 hours

• Prompt engineering strategies for guiding assistant behavior
• Implementing the ReAct framework
• Decomposing multi-step tasks
• Writing effective prompts for planning and execution

Module 4: Assistant Applications and Deployment

Estimated time: 4 hours

• Real-world applications in business, education, coding, and research
• Deployment strategies for generative assistants
• Building assistants for customer interaction and writing tasks
• Testing deployed assistant functionality

Module 5: Evaluation, Ethics, and Design Considerations

Estimated time: 4 hours

• Evaluation metrics for AI assistants
• Addressing bias, safety, and transparency
• User-centered design principles
• Implementing responsible AI practices

Module 6: Final Project

Estimated time: 6 hours

• Design a generative AI assistant for a real-world scenario
• Implement tools, memory, and planning capabilities
• Submit a tested and documented assistant with ethical considerations applied

Prerequisites

• Basic knowledge of Python programming
• Familiarity with large language models (LLMs)
• Access to OpenAI or similar LLM APIs (may involve cost)

What You'll Be Able to Do After

• Build and evaluate intelligent generative AI assistants
• Integrate external tools and retrieval systems into assistant workflows
• Apply prompt engineering and task planning techniques effectively
• Deploy assistants for real-world applications in writing, research, and customer support
• Design and implement ethical, transparent, and user-centered AI assistants