Why LangChain simplifies LLM application development - See It in Action

Why LangChain simplifies LLM application development

📖 Scenario: You want to build a simple app that uses a large language model (LLM) to answer questions. LangChain helps by organizing your code and making it easy to connect to LLMs and other tools.

🎯 Goal: Build a small LangChain app that sets up a language model, configures a prompt, runs the model with the prompt, and shows the answer.

📋 What You'll Learn

Create a LangChain LLM instance with OpenAI

Set a prompt template with a question

Run the LLM with the prompt to get an answer

Print or return the answer

💡 Why This Matters

🌍 Real World

LangChain is used to build chatbots, question-answering apps, and other tools that use language models in a clean, reusable way.

💼 Career

Knowing LangChain helps developers quickly create applications that use AI language models, a skill in growing demand in software jobs.

1

DATA SETUP: Import LangChain and create an OpenAI LLM instance

Write code to import OpenAI from langchain.llms and create a variable called llm that is an instance of OpenAI with temperature=0.

LangChain

# Your code here to import OpenAI and create llm with temperature=0

Need a hint?

Use from langchain.llms import OpenAI and then llm = OpenAI(temperature=0).

2

CONFIGURATION: Create a prompt template with a question

Write code to import PromptTemplate from langchain.prompts and create a variable called prompt that is a PromptTemplate with input_variables=['question'] and template='Answer this question: {question}'.

LangChain

from langchain.llms import OpenAI

llm = OpenAI(temperature=0)
# Your code here to import PromptTemplate and create prompt

Need a hint?

Use PromptTemplate with input_variables=['question'] and the template string.

3

CORE LOGIC: Format the prompt with a question and get the LLM response

Write code to create a variable called formatted_prompt by calling prompt.format(question='What is LangChain?'). Then create a variable called answer by calling llm(formatted_prompt).

LangChain

from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate

llm = OpenAI(temperature=0)
prompt = PromptTemplate(input_variables=['question'], template='Answer this question: {question}')
# Your code here to format prompt and get answer

Need a hint?

Use prompt.format() with the question, then call llm() with the formatted prompt.

4

COMPLETION: Print the answer from the LLM

Write code to print the variable answer.

LangChain

from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate

llm = OpenAI(temperature=0)
prompt = PromptTemplate(input_variables=['question'], template='Answer this question: {question}')

formatted_prompt = prompt.format(question='What is LangChain?')
answer = llm(formatted_prompt)
# Your code here to print answer

Need a hint?

Use print(answer) to show the LLM's response.