Gain foundational knowledge of GenAI, including:

• What is Generative AI and its business value
• Types of GenAI
• Overview of popular GenAI tools
• How GenAI fits into digital transformation

Learn the basics of Large Language Models (LLMs) and build your first GenAI project:

• Setting up OpenAI/Hugging Face APIs
• Model types
• Creating your first prompt-driven application
• Using playgrounds and notebooks for prototyping

Dive into the art of prompt engineering for effective outputs:

• Principles of good prompting
• Chain-of-thought prompting and role-based prompting
• Handling long context and memory in conversations
• Best practices for reproducibility and reliability

Learn how to adapt AI models with your data:

• Preparing and cleaning datasets
• Embeddings and vector databases
• Fine-tuning vs. Retrieval-Augmented Generation (RAG)
• Practical examples: domain-specific AI assistants

Build intelligent AI agents that interact with real systems:

• Automating workflows using GenAI APIs
• Integrating AI with web scraping & APIs
• Handling authentication and secure access
• Multi-agent systems for task orchestration

Master techniques to manage unpredictability in AI:

• Detecting and handling hallucinations
• Logging and monitoring AI interactions
• Guardrails and responsible AI practices
• Debugging prompts and improving reliability

Enterprise-grade GenAI deployment strategies:

• Building scalable pipelines with LangChain, LlamaIndex
• Managing secrets, credentials, and APIs
• Scheduling and automating GenAI tasks
• Monitoring, alerts, and cost optimization

End-to-end solution development covering:

• Identifying a real-world AI use case
• Designing solution architecture with LLMs
• Implementing prompt engineering, RAG, and orchestration
• Documenting and presenting AI solutions
• Deployment & maintenance strategies