KodeKloud Notes

In this lesson, we’ll dive into prompt templates—parameterized messages that make your LangChain interactions more flexible, consistent, and reusable. Instead of hard-coding each system, human, or AI message, you define templates with placeholders that are filled in at runtime.

What Are Prompt Templates?

A prompt template is a blueprint for your LLM interactions. You can:

Define system, human, or AI messages with named placeholders
Populate placeholders at runtime, generating dynamic prompts
Chain multiple templates together, passing data from one step to the next
Standardize messaging across your team for a consistent LLM experience

Note

Always verify that all placeholders match the input keys you supply at runtime to avoid template rendering errors.

Benefits of Using Prompt Templates

Benefit	Description	Example
Reusability	Create once, reuse across multiple chains	`greeting = "Hello, {user_name}!"`
Consistency	Enforce a standard messaging format	System prompts that always start with company branding
Flexibility	Quickly swap out placeholders or default values	Changing `{user_name}` to `{customer_name}` globally
Maintainability	Easier updates when requirements evolve	Update tone or style in one template file

Core Components

System Message Template
Typically sets the overall behavior of the LLM.

from langchain.prompts import SystemMessagePromptTemplate

system_template = SystemMessagePromptTemplate.from_template(
    "You are a helpful assistant for {company_name} support."
)

Human Message Template
Captures user input or queries.

from langchain.prompts import HumanMessagePromptTemplate

human_template = HumanMessagePromptTemplate.from_template(
    "Hi, my order #{order_id} is delayed. Can you help?"
)

AI Message Template
Formats or parses the LLM’s response.

from langchain.prompts import AIMessagePromptTemplate

ai_template = AIMessagePromptTemplate.from_template(
    "The estimated delivery date for order {order_id} is {delivery_date}."
)

The image is a diagram illustrating the flow of an AI message between an application and a chat model, using a prompt template.

Demo Scenarios

In the following demos, we’ll cover:

Creating prompt templates with placeholders
Generating parameterized prompts
Building reusable, organization-wide templates

Let’s jump into the hands-on examples and see these prompt templates in action!

Links and References

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