3-days Instructor-led

Course Description 

This 3-day course explores the rapidly evolving field of Generative AI, with a focus on language models, image generation, and real-world applications. Participants will learn how generative systems create new content—from text to visuals—and how to apply these tools within enterprise workflows. The course includes hands-on labs using Python, Hugging Face Transformers, and Ask Sage, a platform for conversational AI integration and enterprise knowledge retrieval. 

Key Takeaways 

• Explore how generative models work, including LLMs and diffusion-based systems 

• Use pre-trained models to generate and manipulate content 

• Learn prompt engineering techniques for effective AI interaction 

• Apply Ask Sage for knowledge retrieval, document-based Q&A, and AI-powered automation 

• Address ethical considerations and evaluate risks related to AI-generated content 

• Gain hands-on experience with real tools and use cases across multiple domains 

Prerequisites 

• Basic programming experience in Python 

• Familiarity with concepts in AI or data science is helpful 

• Interest in working with LLMs, chatbots, or intelligent content generation 

Module 1: Foundations of Generative AI and Prompt Engineering 

• Overview of Generative AI: From GPT to Stable Diffusion 

• Key Architectures: Transformers, Attention Mechanisms, and Autoencoders 

• Capabilities and Limitations of LLMs 

• Introduction to Prompt Engineering: Precision, Style, and Control 

• Use Cases: Text Generation, Summarization, and Document Drafting 

• Hands-On Lab: Generate responses and summaries using Hugging Face Transformers and OpenAI GPT models 

Module 2: Applied Generative AI with Ask Sage 

• Introduction to Ask Sage: Enterprise Use Cases and Capabilities 

• Connecting Ask Sage to Private Knowledge Repositories 

• Designing Domain-Specific Q&A Systems 

• Incorporating Ask Sage into Workflows (e.g., ticketing, knowledge bases, compliance) 

• Hands-On Lab: Build an Ask Sage assistant for internal knowledge retrieval using sample datasets 

Module 3: Advanced Applications and Ethics in Generative AI 

• Image, Audio, and Multimodal Generation Techniques 

• Evaluation of AI Outputs: Coherence, Originality, and Risk 

• Ethical Implications: Copyright, Misinformation, Deepfakes, and Hallucination 

• Building Responsible AI: Guardrails, Filters, and Human Oversight 

• Final Integration Techniques: APIs, Automation, and Deployment Planning