Description:

Powerful AI solutions are no longer limited to data scientists and engineers. In this fast-paced 90-minute executive workshop, you will learn how to design and deploy a secure, enterprise-ready private AI assistant—without writing code.

Using the Retrieval-Augmented Generation (RAG) architecture and the no-code automation platform n8n, you will build an AI system that answers questions based exclusively on your organization's documents and knowledge assets. The session focuses on practical implementation: connecting documents to an AI “brain,” structuring knowledge for accurate retrieval, and deploying a conversational interface that respects data privacy and business guardrails.

By the end of the workshop, participants will have built a working private AI chat interface capable of answering questions using their own knowledge base—ready to support use cases such as internal knowledge assistants, HR support bots, and sales enablement tools

Duration:

Half Day

Course Code: BDT 540

Learning Objectives:

After this course, you will be able to:

1. Clearly explain the 4-step RAG architecture and its business value to stakeholders
2. Configure LLM, Vector Store, and Memory components using a no-code workflow environment
3. Build an automated pipeline that ingests business documents and converts them into searchable AI knowledge

Business Analysts, Product Managers, Knowledge Managers, Content Creators, Consultants, and non-technical founders who want to leverage AI with their proprietary business data.

Participants should have a basic understanding of Large Language Models (LLMs) and have n8n (Desktop or Cloud) installed. No coding experience is required

Course Outline:

1. The RAG Blueprint for Business
   1. The "Privacy First" AI: Why RAG beats fine-tuning for data
   2. The 4-Step Flow: Input → Retrieval → Augmentation → Generation
   3. n8n Quick-Start: A tour of the AI nodes (LLMs, Memory, and Vector Stores)
   4. Demo: A 2-minute building of a basic "General AI" vs. a "Private Data AI"

2. Building the Knowledge Base: Ingestion
   1. Teaching the AI to Read: Overview of Document Loaders and Embeddings
   2. The "Chunking" Secret: Why breaking documents into pieces improves accuracy
   3. Vector Store Setup: Connecting to a no-code database
   4. Lab: Building an automated pipeline that "reads" a PDF and saves it to the "Brain"

3. Activating the Chatbot: The Retrieval Loop
   1. The AI Agent Node: Configuring the "Retriever" to talk to your database
   2. Memory & Context: Enabling the bot to remember the conversation history
   3. The "Grounded" Prompt: Writing system instructions that prevent the bot from "making things up"
   4. Lab: Linking your Chat Agent to the vector database built in Section 2

4. Activating the Chatbot: The Retrieval Loop
   1. Stress Testing: Asking "out-of-bounds" questions to ensure it stays on task
   2. Personality & Rules: Adjusting the System Prompt for specific business roles

Training material provided: Yes (Digital format)

Hands-on Lab: Students will be provided with docker compose file and n8n workflow JSON.