If you're not learning this AI skill, you're already behind

LangChain is your fast-track to AI-driven development. Here's how to start learning it.

AI is evolving quickly. If you're not learning how to build with it, you're already behind.

Luckily, catching up has never been easier.

A decade ago, building with AI required a deep knowledge of machine learning. But today, all you need to know is how to integrate large language models (LLMs) into your applications.

…That’s where LangChain comes in.

LangChain is an open-source framework that makes it absurdly easy to build AI-powered applications.

With companies racing to ship AI-powered features, knowing LangChain isn’t just an advantage — it’s a must-have.

The good news? If you can write Python and make API calls, you’re ready to get started with LangChain.

Why you need LangChain

LangChain is the fastest way to bring AI into your applications. Instead of writing complex logic to manage prompts, call APIs, and handle memory, LangChain provides a framework that connects LLMs with APIs, databases, and search engines. With pre-built tools, you can:

• Manage conversations and memory

• Retrieve relevant information from your data

• Connect AI to external services

• Automate multi-step workflows

And the best part? You can do all of this with just a few lines of code.

By mastering LangChain, you’ll be able to build AI-powered automation, autonomous agents, chatbots, and smarter search tools (the very features companies are racing to implement).

How LangChain fits into your workflow

1. Set up an LLM

First, you'll need to set up an API key for your provider of choice.

export OPENAI_API_KEY="your_api_key_here"

Then, instead of making raw API calls, you can simply access an LLM in a single line:

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4")

response = llm.invoke("What are some use cases for LangChain?")

print(response)

Boom. You’re already using AI.

2. Integrate with APIs and external tools

Want your AI to do more than just answer questions? LangChain lets you connect LLMs to external tools, like web searches, APIs, or even your own functions:

from langchain_community.tools import DuckDuckGoSearchRun 
search = DuckDuckGoSearchRun()  

# Use an AI agent to fetch live information
result = search.invoke("Latest AI trends 2025")  
print(result)

Just like that, you’ve built an AI-powered research assistant.

3. Chain components together for smarter AI

AI applications rarely work in isolation. Instead of just passing a single prompt to an LLM, you often need to retrieve relevant information first (e.g., from a database, API, or local documents).

This is where Retrieval-Augmented Generation (RAG) comes in. RAG allows your AI to fetch external data before responding, leading to more accurate and context-aware answers.

Below, we chain multiple components together to build a question-answering system that first retrieves the most relevant data and then passes it to an LLM for reasoning:

from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_core.prompts import PromptTemplate
from langchain_community.vectorstores import Chroma
from langchain_community.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains.question_answering import load_qa_chain

# Create vector DB from data
documents = TextLoader("data.txt").load()
texts = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0).split_documents(documents)
db = Chroma.from_documents(texts, OpenAIEmbeddings())

# Create simple QA system
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
qa = load_qa_chain(llm, chain_type="stuff")

# Ask questions
question = "This is where you ask your question"
docs = db.similarity_search(question)
result = qa.invoke({"input_documents": docs, "question": question})
print(result['output_text'])

Now your LLM isn’t just generating text. It has memory and retrieval.

Ready for LangChain?

By understanding LangChain, you'll secure your spot among the developers behind the next generation of AI-powered applications.

The best way to get started? Learn the fundamentals and start applying them right away:

1. Get hands-on with LangChain’s core building blocks: Learn how to structure prompts, chain LLM calls, and manage memory.

2. Explore retrieval and AI-powered workflows: Use vector databases to give LLMs real-world context.

3. Build projects: Create chatbots, search tools, and AI-powered automations.

You can kick off your LangChain journey with this popular course on Educative: Unleash the Power of Large Language Models Using LangChain.

In this hands-on course, you'll learn how to:

• Use prompt templates, chains, and memory to manage conversational AI.

• Connect LLMs to external tools and APIs for automation.

• Build AI agents that dynamically take actions.

• Use RAG (Retrieval-Augmented Generation) to power smarter question-answering.

• Explore LangGraph to build multi-agent AI systems.

Which AI-related topic would you like me to cover next?

Reply or leave a comment to let me know.

Happy learning!

—Fahim