Domains

Objective

Scenario

Tools & Activities:

Outcome

Build and deploy a Policy Chatbot that can instantly, and reliably answer complex employee questions by citing the exact sections of HR policy documents.

Who Should Attend

HR Specialist, Global HR Team

Use Case

An AI system uses (Retrieval-Augmented Generation) RAG to search a database of HR policies and generate a concise answer while simultaneously providing the source location (page/section/document name) of the policy.

Core Challenges

Tools & Activities:

The course explores 

• • How to setup a RAG architecture to explore an HR Policy document and ground the respond within the information available in a given input (HR Policy document)
  • • Document Ingestion and Chunking Strategy, Vector Database Setup and Indexing, Implementing the RAG Workflow
  • Prompt Engineering.
  • Interacting with an LLM through the chat interface
  • Pinecone vector database configuration, chunking and storage, search and retrieval
  • n8n workflow automation
  • Building a Chat interface

Outcome

Participants will gain the skills to deploy a Trustworthy, Authoritative HR Policy Chatbot, leading to significantly reduced HR workload, instant 24/7 availability, and improved compliance through standardized policy interpretation.

To understand and implement Retrieval-Augmented Generation (RAG) as a key architectural pattern for LLMs to deliver evidence-based, trustworthy Q&A from vast medical literature.

Use Case

Building and querying an AI system that leverages a proprietary medical knowledge base to provide referenced and highly accurate answers to clinical and research inquiries.

Core Challenges

Information Overload: Healthcare SMEs struggle to manually review thousands of new papers to stay current.

Slow Decision Support: Traditional search methods are slow and do not provide concise, synthesized answers with source evidence.

Hallucination Risk: Typical / standard LLMs could respond with inaccurate or fabricated information.

Tools & Activities:

The course explores 

• • How to setup a RAG architecture to explore a medical paper and ground the respond within the information available in a given input (pdf research paper)
  • Prompt engineering.
  • Interacting with an LLM through the chat interface
  • Pinecone vector database configuration, chunking and storage, search and retrieval
  • n8n workflow automation

Outcome

Participants will gain the skills to deploy a trustworthy, evidence-based AI system that ensures high factual accuracy and provides instant, referenced answers to complex clinical or research questions.

This course teaches you how to build an automated Risk Impact Analysis workflow in n8n. By uploading a Microsoft Project XML baseline along with a Slack conversation describing a delay or issue, the workflow extracts task information, identifies the impacted activity, and evaluates the effect of the risk on project delivery. It then generates a clear, structured HTML risk report that appears directly in the form output. Project managers can instantly understand how real-world events influence planned work, helping them document delays, communicate impacts, and respond to risks without manual analysis.

This course teaches you how to build an automated Critical Path Method (CPM) schedule analysis workflow in n8n. By uploading a Microsoft Project XML file, the workflow extracts task data, performs CPM calculations, identifies the critical path, and generates a complete HTML status report that appears directly in the form output. Project managers can instantly understand schedule health, risks, and task dependencies without manual analysis.