Build No-Code LLM Applications

Enroll in this training and receive a one-month complimentary e-learning subscription with access to 40+ courses.

This course is provided by Big Data Trunk for Stanford Technology Training Program but a limited few seats available to the public.

Students of this class may have opportunity to be considered for Internship with Big Data Trunk.

Have you ever wished you could build your own AI-powered app like ChatGPT — without needing to know how to code? Whether you're a product manager, business analyst, QA professional, or a curious technologist, this one-day immersive workshop will empower you to turn that vision into reality.
“Build LLM Powered Applications with No-Code” is your gateway into the world of Generative AI and Large Language Models (LLMs), designed specifically for professionals without a technical background. You’ll gain practical, hands-on experience using Lang flow — a powerful visual, drag-and-drop tool that lets you design and deploy LLM applications without writing a single line of code**.**
You'll also dive into advanced concepts like Retrieval Augmented Generation (RAG) and Agentic-AI, all within the visual builder environment Lang flow provides. With Lang flow, you can prototype and deploy powerful applications in minutes — making it ideal for professionals looking to build, test, and iterate fast.
Whether you're looking to enhance workflows, build customer-facing AI solutions, or just stay ahead in your career, this course gives you the foundation to do so — quickly, creatively, and confidently.

Learning Objectives:
After this course, you will be able to:

• Understand what LLMs are and how they are used in industry
• Craft effective prompts and use prompt templates (zero, one, few-shot)
• Build conversational experiences using data in various formats (CSV, PDF, etc.)
• Use agents to complete multi-step tasks automatically
• Build RAG applications using embeddings and vector databases
• Create multi-agent workflows using Agentic-AI principles

#### Course Outline:

1. Introduction to Large Language Models (LLMs)
2. A brief history of Generative AI and LLMs
3. What is a Large Language Model?
4. Why LLMs are disrupting industries
5. Hands-on: Create the lab environment and work with LLM
6. Using Model Prompts and Prompt Templates
7. What is a prompt?
8. Types of prompts: zero-shot, one-shot, few-shot
9. Hands-on: Build a simple chat application using prompt and prompt templates
10. Build a captivating conversation using different types of documents formats
11. Introduction to data connectors
12. Uploading and interacting with CSV, PDF, and other formats
13. Hands-on: Load a CSV file and get insights in the data
14. Using Agents to perform tasks
15. What are agents in context of AI?
16. Types of agents: tool-using agents, reasoning agents, reactive agents
17. Hands-on: couple of labs using agents
18. Building RAG applications
19. What is Retrieval Augmented Generation (RAG)?
20. Why RAG is important for custom knowledge applications
21. Understanding embeddings and vector stores
22. Hands-on: Build an RAG application with vector storage and embeddings
23. Using Agentic-AI to build multi-agent applications
24. What is Agentic-AI?
25. How agents can collaborate to perform complex workflows
26. Tools that make multi-agent applications possible in no-code platforms
27. Hands-on: Build financial report application using multiple agents

Training material provided: Yes (Digital format)

Hands-on Lab: Students can use either open-source models or OpenAI models. Instructions will be provided to install tools for local machines or a website build use (free of charge) to build applications

Date & Time:
12/09/25 : 1 to 4 pm PST
12/12/25 : 1 to 4 pm PST