> ## Documentation Index
> Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Course Introduction
> Introductory course teaching how to build modern AI applications with LangChain, covering models, prompts, chains, memory, tools, agents, and hands-on labs
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).
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`.
```python theme={null}
# 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.
```python theme={null}
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
* [LangChain Documentation](https://langchain.readthedocs.io/)
* [OpenAI API Documentation](https://platform.openai.com/docs)
* [KodeKloud Community](https://kodekloud.com/community)
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.