Description:

Retrieval-Augmented Generation (RAG) has emerged as the industry standard for building AI systems that are both intelligent and secure—capable of delivering accurate, context-aware responses based exclusively on your organization’s private data.

In this hands-on, half-day intensive workshop, participants will learn how to design and deploy a fully functional private AI chatbot without writing a single line of code. Using n8n, a powerful workflow automation platform, you will build a production-ready RAG system that connects your business documents (PDFs, Google Docs, websites, and more) to advanced language models from providers such as Gemini/OpenAI/Open-Source.

Rather than focusing on complex programming or theoretical concepts, this course emphasizes practical implementation. You will learn how to create embeddings, configure vector databases, implement retrieval pipelines, and deploy a secure chat interface that answers questions strictly from your curated knowledge base—protecting sensitive information while increasing operational efficiency.

By the end of the session, you will have built and tested your own private AI assistant tailored to a real-world business use case, ready for deployment to your website, internal teams, or customer support channel.

Duration:

Half Day

Course Code: BDT 538

Learning Objectives:

After this course, you will be able to:

1. Understand the 4-step RAG architecture
2. Configure AI nodes in n8n (LLMs, Memory, and Vector Stores)
3. Upsert private documents into a Vector Database without code
4. Deploy a functional chat interface that queries your private data
5. Implement basic "guardrails" to keep the bot on task

Business Analysts, Product Managers, Content Creators, and non-technical entrepreneurs who want to build custom AI solutions without writing code.

Basic understanding of AI concepts (LLMs) and ability to run n8n locally or via Docker. No coding experience required.

Course Outline:

1. Foundations of No-Code AI & RAG
   1. What is RAG and why does your business need it?
   2. Introduction to the n8n Canvas: Nodes, Triggers, and Actions
   3. Lab: Connecting your first AI model (local or cloud based) to n8n

2. The "Brain" and the "Library": Embeddings & Vector Stores
   1. How AI "reads" documents: Understanding Embeddings
   2. Choosing a Vector Database: Explore different types of vector store (cloud based or local)
   3. Lab: Building an automation that automatically "reads" and saves a PDF into the database

3. Building the Ingestion Pipeline
   1. Document Loaders: How to pull data from PDFs, Google Drive, or Websites
   2. The "Chunking" Strategy: Why size matters for AI accuracy
   3. Lab: Building an automation that automatically "reads" and saves a PDF into the database

4. Creating the Chat Interface
   1. Using the "AI Chat Agent": node in n8n
   2. Connecting the Retriever: Linking the chat to your Vector Store
   3. Adding "Memory": Ensuring the bot remembers the previous question
   4. Lab: Testing your chatbot with "out-of-bounds" questions to ensure privacy

5. Advanced Tweaking & Deployment
   1. System Prompts: Giving your bot a personality and specific rules
   2. Exporting: your bot to a website
   3. Lab: Final project—Customizing your bot for a specific use case (e.g., HR Assistant or Customer Support)

Training material provided: Yes (Digital format)

Hands-on Lab: Instructions will be provided to set up n8n and API keys.