Partial prompt templates in LangChain - Mini Project: Build & Apply

Building a Partial Prompt Template with LangChain

📖 Scenario: You are creating a chatbot that answers questions about movies. You want to reuse parts of the prompt that describe the chatbot's role and style, while allowing the movie title to change dynamically.

🎯 Goal: Build a partial prompt template using LangChain that fixes the chatbot's role and style, and later add the movie title dynamically to complete the prompt.

📋 What You'll Learn

Create a partial prompt template with fixed role and style

Add a variable placeholder for the movie title

Use the partial prompt template to create a full prompt by filling in the movie title

Ensure the prompt template is valid and usable with LangChain's PromptTemplate

💡 Why This Matters

🌍 Real World

Partial prompt templates let you reuse fixed parts of prompts and fill in dynamic data later, which is common in chatbots and AI assistants.

💼 Career

Knowing how to build and use partial prompt templates is useful for AI developers and prompt engineers working with LangChain or similar frameworks.

1

Create a partial prompt template with fixed role and style

Import PromptTemplate from langchain.prompts and create a partial prompt template called partial_prompt with the template string: "You are a helpful assistant who provides detailed answers about movies. Your style is friendly and informative."

LangChain

# Your code here

Need a hint?

Use PromptTemplate and set the template argument to the fixed text.

2

Add a variable placeholder for the movie title

Modify the partial_prompt template string to include a variable placeholder called {movie_title} at the end, so the template becomes: "You are a helpful assistant who provides detailed answers about movies. Your style is friendly and informative. Tell me about the movie {movie_title}."

LangChain

from langchain.prompts import PromptTemplate

# Add {movie_title} placeholder to the template string
partial_prompt = PromptTemplate(template="You are a helpful assistant who provides detailed answers about movies. Your style is friendly and informative. Tell me about the movie {movie_title}.")

Need a hint?

Insert {movie_title} exactly as shown inside the template string.

3

Create a full prompt by filling in the movie title

Use the partial_prompt to create a full prompt by calling its format method with movie_title='Inception' and assign the result to a variable called full_prompt.

LangChain

from langchain.prompts import PromptTemplate

partial_prompt = PromptTemplate(template="You are a helpful assistant who provides detailed answers about movies. Your style is friendly and informative. Tell me about the movie {movie_title}.")

# Create full_prompt by formatting partial_prompt with movie_title='Inception'
full_prompt = partial_prompt.format(movie_title='Inception')

Need a hint?

Call partial_prompt.format() with the argument movie_title='Inception'.

4

Use the full prompt with LangChain's LLMChain

Import LLMChain and OpenAI from langchain. Create an llm instance using OpenAI(). Then create an llm_chain using LLMChain with the llm and partial_prompt. Finally, call llm_chain.run with movie_title='Inception'.

LangChain

from langchain.prompts import PromptTemplate
from langchain import LLMChain, OpenAI

partial_prompt = PromptTemplate(template="You are a helpful assistant who provides detailed answers about movies. Your style is friendly and informative. Tell me about the movie {movie_title}.")

full_prompt = partial_prompt.format(movie_title='Inception')

# Create llm, llm_chain and run llm_chain with movie_title='Inception'
llm = OpenAI()
llm_chain = LLMChain(llm=llm, prompt=partial_prompt)
llm_chain.run(movie_title='Inception')

Need a hint?

Import LLMChain and OpenAI, create instances, then run the chain with the variable.