• Why AI matters for working professionals
• Evolution of AI systems
• Handling unstructured text input
• How LLMs and agents became the new AI workflow stack

• Data fundamentals for AI workflows
• ML concepts you must know for real use cases
• What makes an AI system work end-to-end
• How ML, LLMs, retrieval, and agents connect in real systems

• Understanding AI workflow building blocks
• Working with APIs, structured inputs, and outputs
• Handling unstructured text and documents
• Organising workflows using templates and reusable components

• Supervised and unsupervised learning basics
• Building and validating small ML models using standard tools
• Evaluating model behaviour with simple metrics
• Exporting and reusing models in practical workflows

• Using LLMs safely and efficiently for workplace tasks
• Prompt patterns for reasoning and structured outputs
• Understanding context windows and token limits
• Using LLMs for summaries, transformations, and workflow automation

• Document loading, chunking, and preprocessing
• Embeddings and vector search basics
• Connecting retrieval to prompts for reliable outputs
• Building a simple RAG workflow end-to-end

• What tool calling means and how it works in practice
• Designing controlled tool inputs and outputs
• Building multi-step agent workflows
• Agent output checking and validation patterns

• Turning workflows into usable internal tools
• Simple interfaces for teams and stakeholders
• Environment setup and dependency basics
• Introduction to monitoring, logging, and observability

• Evaluating AI outputs for accuracy and reliability
• Improving retrieval and prompting strategies
• Error handling and fallback mechanisms
• Basic safety, guardrails, and controlled execution principles

Participants build an end-to-end Agentic AI solution that includes:

• LLM usage and workflow logic
• Retrieval logic (RAG) where relevant
• Tool-based workflow or agent orchestration
• A usable interface or internal tool output
• Clear documentation and demonstration