What are PromptTemplates in LangChain?

PromptTemplates are objects that format dynamic inputs into the correct structure before passing them to a model. Instead of building prompt strings with f-strings scattered across your codebase, templates give you reusable, testable, versionable prompt construction with named variables.

There are two main types:

• PromptTemplate — produces a plain text string. Best for LLMs (non-chat models):

from langchain_core.prompts import PromptTemplate
pt = PromptTemplate.from_template("Summarise this in {n} sentences: {text}")
print(pt.format(n=2, text="LangChain is..."))

• ChatPromptTemplate — produces a list of typed messages. Best for Chat Models:

from langchain_core.prompts import ChatPromptTemplate
chat_pt = ChatPromptTemplate.from_messages([
    ("system", "You are a {role}."),
    ("human", "{user_input}"),
])
messages = chat_pt.format_messages(role="poet", user_input="Write about the sea.")

MessagesPlaceholder is used inside a ChatPromptTemplate to insert a variable-length list of messages — useful for injecting conversation history. partial() lets you pre-fill some variables while leaving others to be filled at call time, which is handy for re-usable templates across different contexts.

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