Getting Started with LangChain : A Beginner’s Guide to LLM-Powered Applications

In my journey to explore the world of Generative AI and Large Language Models (LLMs), I recently began learning LangChain a powerful framework that makes it easier to build intelligent, data-aware applications with LLMs.

This post shares my beginner level understanding of LangChain, its purpose, and some of the core components that form its foundation.

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🧠 What is LangChain?

LangChain is an open-source framework designed to simplify the process of building applications that leverage the capabilities of LLMs like OpenAI's GPT.

It allows developers to:

• Chain together multiple components like prompts, memory, tools, and agents.

• Interact with APIs, documents, or custom functions using LLMs.

• Build powerful use cases like chatbots, question-answering systems, and RAG-based apps.

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🔗 Core Concepts I Learned

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🧪 Sample Concept : Prompt Template

One of the most useful tools I discovered is the Prompt Template. Here's a basic example in Python:

from langchain.prompts import PromptTemplate

template = "Tell me a {adjective} joke about {subject}"
prompt = PromptTemplate(template=template, input_variables=["adjective", "subject"])

print(prompt.format(adjective="funny", subject="AI"))