No-Code AI Bootcamp: Build LLM Applications

Description:

AI is no longer a futuristic concept — it’s transforming how businesses operate today. But building AI-powered applications has traditionally required programming skills. Not anymore.

This 10-week No-Code AI Bootcamp empowers you to design, build, and deploy AI-driven applications using LangFlow (a drag-and-drop LLM app builder) and n8n (a no-code workflow automation platform). Over the course of ten Saturdays, you’ll go from understanding the fundamentals of Large Language Models (LLMs) to building advanced, production-ready applications — all without writing a single line of code.

In addition to core skills like prompt engineering and Retrieval Augmented Generation (RAG), you’ll explore the fast-growing frontier of Agentic AI — intelligent agents that can reason, plan, and collaborate to complete complex, multi-step workflows. This is the next wave of AI, and you’ll gain hands-on experience building your own agent-driven applications.

Through a mix of guided instruction, hands-on labs, and project-based learning, you will:

• Master the art of prompting and conversation design.
• Connect LLMs with real-world data sources like PDFs, CSVs, and APIs.
• Build intelligent agents, RAG applications, and Agentic AI workflows.
• Automate business processes by integrating AI apps with tools Search APIs, web page scrappers, etc.
• Develop and present a capstone project that showcases your ability to apply AI in real business or creative scenarios

Duration:

5 days ( 5 full days or 10 half days or 1 day a week for 5 weeks)

Course Code: BDT 528

Learning Objectives:

After this course, you will be able to:

1. Understand what LLMs are, how they work, and why they matter
2. Craft effective prompts and build reusable prompt templates
3. Connect LLMs with structured/unstructured data (CSV, PDF, APIs, websites)
4. Design and deploy agents to perform multi-step tasks
5. Build Retrieval Augmented Generation (RAG) apps with embeddings and vector databases
6. Create collaborative multi-agent workflows using Agentic-AI principles
7. Use n8n to automate workflows and connect AI apps with real-world tools
8. Build and present a capstone AI app with both LangFlow and optional n8n integration
9.  

Non-coding software engineers, business analysts, QA professionals, product managers, and technologists who want to design and deploy Generative AI applications without writing code — no machine learning experience required.

No programming experience required, experience playing with ChatGPT or equivalent LLMs will be nice but not required

Course Outline:

1. Foundations of Generative AI and LLMs
   1. History and impact of LLMs
   2. Key terminology: tokens, parameters, training, inference
   3. Why LLMs disrupt industries and workflows
   4. Hands-on: Set up LangFlow workspace and run your first LLM interaction
2. The Art of Prompting
   1. What makes a good prompt?
   2. Zero-shot, one-shot, few-shot prompting
   3. Prompt templates and variables
   4. Hands-on: Build a simple Q&A chatbot with structured prompts
3. Designing Conversations with LLMs
   1. Conversational design principles
   2. Role prompts, context windows, memory
   3. Hands-on: Build a chatbot that maintains context across turns
4. Connecting Data Sources
   1. Introduction to data connectors (CSV, PDF, API, websites)
   2. Pre-processing and cleaning data for LLMs
   3. Hands-on: Upload a CSV and a PDF, extract insights through LangFlow
5. Agents for Automation
   1. What are AI agents?
   2. Tool-using agents, reasoning agents, reactive agents
   3. Hands-on: Build an agent that automates a multi-step research workflow
6. Retrieval Augmented Generation (RAG)
   1. What is RAG and why is it powerful?
   2. Embeddings and vector stores explained simply
   3. Hands-on: Build a knowledge-based chatbot using embeddings + vector DB
7. Building Agentic AI Applications
   1. What is Agentic AI and why is it the next big wave?
   2. How multi-agent collaboration works
   3. Tools for building agent workflows
   4. Hands-on: Multi-agent financial report generator
8. Workflow Automation with n8n
   1. Introduction to n8n: visual workflow builder
   2. Integrating LangFlow applications into n8n workflows
   3. Connecting LLM outputs with real-world tools (Search APIs, Web Scrappers, etc)
   4. Hands-on: Build a pipeline using n8n to summarize website content
9. Capstone Project (Design & Build)
   1. Students form small groups or work solo
   2. Choose a project idea (see below)
   3. Begin building full end-to-end application with data + agents + RAG
   4. Option: Add n8n integration for workflow automation
   5. Instructor and peer feedback during build phase
10. Capstone Showcase & Career Next Steps
    1. Final project presentations
    2. Peer and instructor feedback
    3. Discussion: How to take your skills into your workplace (prototyping, pitching ideas, building Proof of Concepts)
    4. Resources for continued learning and no-code AI toolkits

Training material provided:

1. Slides (PDF)
2. Lab instructions (step-by-step)
3. Prompt templates & LangFlow starter flows
4. n8n starter workflows for AI integration

Hands-on Lab: Students can use either open-source models or OpenAI models. Instructions will be provided to install tools for local machines