Building AI Assistant Web Apps with Python, LangChain, and Gradio

In this hands-on crash course, you'll learn how to build AI-powered applications using the latest 2025 version of LangChain, Python, and Gradio.

We will dive into coding a LangChain memory-enabled conversational agent using Python. Along the way, you’ll explore prompt engineering, system prompts, API setup, persistent chat history, and how to use LangChain effectively. Whether you're new to AI or a developer exploring LLMs, this beginner-friendly course gives you practical tools to build custom AI apps—even with no Python experience.

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Section 1 - Core AI Concepts

Before diving into code, in this section we’ll explore key AI concepts like LLMs, agents, RAGs, and APIs, so you understand how these systems actually work.

1. ▶️ What Are LLMs? 🔒
   Overview of large language models and how apps like ChatGPT use them

2. ▶️ What Are AI-Powered Apps? 🔒
   How AI apps differ from traditional apps — architecture, roles of APIs, LLMs, and frontend

3. ▶️ What Are RAGs (Retrieval-Augmented Generation)? 🔒
   How to build AI that can "look things up" using your own data

4. ▶️ What Are AI Agents? 🔒
   Explains autonomous agents, tools, and how they chain together reasoning steps

Section 2 – Building an AI Powered App

5. ▶️ How an AI-Powered App Works 🔒
   Explains the architecture: frontend, backend, APIs, and LLMs)

6. ▶️ Your First AI App – Saying Hello 🔒
   Creating your first simple AI app with Python and basic logic

7. ▶️ Building a Chat Loop – Repeat the Conversation 🔒
   Implementing a while loop to keep the conversation going

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Section 3 – Equipping the AI Brain

8. ▶️ Prepping the Brain – Loading the API Key and Setup (Free Preview)
   Setup of LLM API keys or other model APIs with LangChain basics

9. ▶️ Using LangChain to Get AI Replies (Free Preview)
   Basic usage of LangChain to connect prompts and get outputs

10. ▶️ Giving the AI a Personality – System Prompt Engineering 🔒
    Adding a system prompt to give your assistant a custom persona

11. ▶️ Keeping the Conversation Going – Memory 🔒
    Implementing memory using LangChain for longer context

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Section 4 – Persistence and Structure

12. ▶️ Storing Conversation History 🔒
    Saving conversations locally using JSON/CSV or other formats

13. ▶️ LangChain Usage – Updated Best Practices 🔒
    Updated method to use LangChain effectively in latest versions

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Section 5 – Web Interface and Final Touches

14. ▶️ Building the Gradio Web Interface 🔒
    Creating a simple and clean front end for your AI app

15. ▶️ Getting AI Responses in the Web App 🔒
    Hooking the backend logic to the Gradio interface

16. ▶️ Polishing the App 🔒
    Final UX touches, error handling, and deployment tips