Course Introduction

Welcome to the LangChain course. I’m Janakiram MSV, and I’ll be your instructor. This course teaches you how to build modern AI applications using LangChain — the orchestration framework that helps developers connect large language models (LLMs) to databases, APIs, and the web to create powerful generative AI experiences. What you’ll learn

Core building blocks of LLM applications (models, inputs, outputs)
LangChain expression language (LCEL)
Chains, memory, tools, and agents
Prompt engineering and output parsing
Hands-on demos and practical exercises with notebooks

This course highlights common elements across applications like OpenAI ChatGPT, Google Gemini, and Microsoft Copilot and shows how LangChain helps you compose these pieces into full applications. This course covers modules such as model, input, output, and the LangChain expression language (LCEL).

The image shows a person speaking, wearing a KodeKloud T-shirt, with a presentation slide next to them titled "Building Blocks of LLM Apps" and including topics like "Common Elements" and "Key Components of LangChain".

Course format

Theory lecture: concise explanation of the concept
Demo: instructor walkthrough implementing the concept
Practical exercise: hands-on labs managed by KodeKloud

Each topic follows this pattern so you can immediately apply what you learn in the notebooks provided. Example: Building a simple chat prompt Below is a compact example that demonstrates how to build a chat prompt template using LangChain prompt primitives and format it with variables like subject and concept.

# Example: Chat prompt template with system and human messages
from langchain.chat_models import ChatOpenAI
from langchain.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate

sys_msg = "You are a {subject} teacher"
human_msg = "Tell me about {concept}"

prompt_template = ChatPromptTemplate.from_messages(
    [
        SystemMessagePromptTemplate.from_template(sys_msg),
        HumanMessagePromptTemplate.from_template(human_msg),
    ]
)

prompt = prompt_template.format_messages(subject="Chemistry", concept="Periodic Table")
# You can pass `prompt` to a chat model, e.g.:
# model = ChatOpenAI(temperature=0)
# response = model.generate(prompt)

Practical exercises and APIs All hands‑on exercises are included in the course notebooks and managed via KodeKloud. To run them locally, supply your own API keys for OpenAI and any other third-party services used in the labs.

import os

# Set your API key as an environment variable before running notebooks
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

Never commit or share your secret keys. Use environment variables or secret management tools when running notebooks or deploying applications.

Course outcomes When you finish this course you will be able to:

Design prompt templates and chains that combine multiple LLM calls
Implement memory and stateful interactions for multi-turn apps
Use tools and agents to interact with APIs and external systems
Parse and validate model outputs for downstream processing

Resources and references

Modules overview

Module	Primary focus	Example topics
Model	Choosing and using LLMs	Chat models, temperature, streaming
Input	Prompts and prompt templates	System/Human messages, LCEL
Output	Parsing & validating outputs	Output parsers, structured responses
Chains	Composing multi-step flows	Sequential, conditional, map-reduce chains
Memory	Stateful interactions	Conversation memory, vector stores
Tools & Agents	External actions & orchestration	API calls, web search, tool selection

Tip: Follow along with the demos in the provided notebooks and run the practical exercises to reinforce each concept. Use the KodeKloud community forum for questions and peer help.

Let’s jump in and start building modern LLM applications with LangChain.

Introduction

Overview of LangChain

Key Components of LangChain

Interacting with LLMs

Tips, Tricks, and Resources

Introduction to LCEL

Adding Memory to LLM Apps

Performing Retrieval

Implementing Chains

Using Tools

Building Agents

Conclusion

Building Blocks of LLM Apps

Course Introduction

Watch Video