
Explore how predictive models differ from generative models, and how deep learning builds neural networks with layers that learn patterns from data to understand and generate natural language, including llms.
Discover agentic AI fundamentals by seeing how agents use tools and LLM reasoning to achieve goals, automate steps, and relay results between user and model in multi-step tasks.
Refine prompts step by step to tailor responses in real projects, using explicit context like role, experience, and difficulty, then control output structure for targeted results.
Explore the OpenAI platform, including docs, API reference, models, libraries, pricing, and API keys, and learn how to use the chat playground to test prompts with GPT models.
Learn how to add credit to the OpenAI platform to access the API, compare pricing by tokens, and choose cost-effective models like GPT-5 Nano for learning and experimentation.
Choose models for agentic ai by weighing cost, performance, and long-context abilities, test with real prompts, and ensure the model supports step-by-step reasoning per OpenAI documentation.
Install Python and VS Code by downloading from official sites, selecting your OS, running the installers with default settings, and verifying installation with python --version.
Create a Jupyter notebook and load the environment variable from the .env file using the python-.env library, enabling secure access to the OpenAI API without hardcoding.
Demonstrate reusable prompts by defining variables and injecting values, loading the OpenAI api key, creating an OpenAI client, and sending a chat completion to generate variable-driven content.
Learn how to preserve context in LLM conversations by sending the full message history with each follow-up. The demo uses chat completions with user and assistant roles to maintain memory.
Load environment variables and initialize an OpenAI client with GPT 4.1 mini to power a command line chatbot, and implement a getResponseContent helper to send messages and return model's reply.
Build a command line chatbot in Python using a helper function and an OpenAI client to manage a messages list, print the model's reply, and loop until exit.
Set up a Streamlit chatbot environment by installing Streamlit, creating chatbot.py, and running streamlit run to launch a local web app using the OpenAI chat completions API.
Build a streamlit chatbot ui that communicates with an OpenAI model by loading environment variables, creating the OpenAI client, managing a messages list, handling user input, and displaying chat history.
Langchain provides a common interface to switch between model providers like OpenAI, Gemini, and Anthropic with minimal code changes, enabling runnables for fixed workflows or agents for dynamic decision making.
Explore retrieval augmented generation with LangChain, loading PDFs, creating embeddings, storing in a vector store, and retrieving relevant chunks to answer questions via an LLM.
Identify and distinguish the three LangChain message types: human, AI, and system, and map them to OpenAI roles (user, assistant, system) for forward-compatible conversational design.
Demonstrates sending prompts to an llm with langchain, loading environment variables, and using traditional chatopenai and unified initChatModel with a human message.
Build your first chain by linking an LLM to an SDR output parser to parse the model's response and produce a final plain text output.
** Why This Course? **
AI is evolving at an incredible pace.
Every day, you'll come across new terms on LinkedIn, YouTube, blogs, and social media—Agentic AI, AI Agents, RAG, Tools, Middleware, Guardrails, and many more.
For beginners, it can feel like a sea of buzzwords, complex diagrams, and unfamiliar concepts. Many people start learning but quickly become overwhelmed because they don't know where to begin or how everything fits together.
This course is designed to change that.
We'll start by understanding how to communicate with Large Language Models (LLMs) and gradually introduce how to build AI applications. Instead of memorizing jargon, you'll understand why each concept exists, how it works, and when to use it.
By the end of the course, you'll have the confidence to move beyond beginner tutorials and continue exploring modern AI frameworks with a clear understanding of the concepts behind them.
** How You'll Learn **
This course takes you from complete beginner to an intermediate level in LangChain and AI application development.
No prior knowledge of OpenAI, Agents, Agentic AI, RAG, or LangChain is required. Every concept is introduced from scratch using simple language and practical examples.
Each major topic concludes with a hands-on lab where you'll apply what you've learned instead of simply watching videos.
To help you practice effectively, every lab includes:
A complete working solution notebook
A TODO notebook for guided practice
Throughout the course, you'll build and continuously enhance real AI applications through hands-on coding exercises and Python projects.
You'll learn to build a Chatbot, an Interview Preparation Application, and a Chat with Your PDF application. The Interview Preparation App is enhanced step by step as you learn new concepts, helping you understand how to apply the concepts that you have learnt in the course.
To make complex topics easier to understand, carefully designed visual illustrations and diagrams are used throughout the course, making learning more engaging, intuitive, and memorable.
** Skills You'll Gain **
By the end of this course, you'll be able to:
Understand how LangChain simplifies AI application development.
Build AI applications using LangChain and Open AI from scratch.
Create Agentic AI and Retrieval-Augmented Generation (RAG) applications with confidence.
Understand Important AI concepts such as Runnable, Tools, Agents, Middleware, Guardrails and RAG.
Apply these concepts in practical projects instead of simply memorizing theory.
** Course Topics **
AI Foundations
AI basics
OpenAI Python SDK
Build your first chatbot
Core LangChain
Messages
Chains
Prompt templates
Build an "Interview Preparation App"
Runnables
Build workflows using Runnables
Enhance the "Interview Preparation App" using Runnables
Agentic AI
Tools
Agents
Structured outputs
Enhance the "Interview Preparation App" using Agents/Tools
Advanced Features
Streaming
Middleware
Guardrails
RAG
Documents → Embeddings → Vector stores → Retrieval
Chain-based RAG vs Agentic RAG
Build a "Chat with Your PDF" application
By the end of this course
You won't just learn LangChain—you'll gain the confidence to design and build your own AI applications from scratch.
** Meet Your Instructor **
I'm Ruchi, a Corporate Trainer and Technical Instructor with over 20 years of experience in software development, enterprise integration, technical leadership, and professional training.
Before becoming a full-time trainer and course creator, I spent more than 12 years at HCL Technologies, where I worked in technical and leadership roles, delivering enterprise solutions and mentoring development teams. I also received multiple awards from HCL and clients, including GE and BEA Systems, for technical excellence and project contributions.
Today, I specialize in Agentic AI, LangChain, RAG, MuleSoft, Java, and Spring, creating practical, hands-on courses that help learners build real-world skills. More than 5,500 learners have enrolled in my courses.
My teaching philosophy is simple: break down complex concepts into easy-to-understand lessons and reinforce them through hands-on labs, so you gain the confidence to apply what you learn in real-world applications.
** Ready to Build Your First AI Application? **
Don't let AI buzzwords hold you back. Start building real AI applications with LangChain, one step at a time. Enroll today and gain practical, hands-on skills that will continue to serve you long after you complete this course.