Here's the simplest example:

```python
from langchain import PromptTemplate


template = """/
You are a naming consultant for new companies.
What is a good name for a company that makes {product}?
"""

prompt = PromptTemplate.from_template(template)
prompt.format(product="colorful socks")
```

<CodeOutputBlock lang="python">

```
    You are a naming consultant for new companies.
    What is a good name for a company that makes colorful socks?
```

</CodeOutputBlock>


## Create a prompt template

You can create simple hardcoded prompts using the `PromptTemplate` class. Prompt templates can take any number of input variables, and can be formatted to generate a prompt.


```python
from langchain import PromptTemplate

# An example prompt with no input variables
no_input_prompt = PromptTemplate(input_variables=[], template="Tell me a joke.")
no_input_prompt.format()
# -> "Tell me a joke."

# An example prompt with one input variable
one_input_prompt = PromptTemplate(input_variables=["adjective"], template="Tell me a {adjective} joke.")
one_input_prompt.format(adjective="funny")
# -> "Tell me a funny joke."

# An example prompt with multiple input variables
multiple_input_prompt = PromptTemplate(
    input_variables=["adjective", "content"], 
    template="Tell me a {adjective} joke about {content}."
)
multiple_input_prompt.format(adjective="funny", content="chickens")
# -> "Tell me a funny joke about chickens."
```

If you do not wish to specify `input_variables` manually, you can also create a `PromptTemplate` using `from_template` class method. `langchain` will automatically infer the `input_variables` based on the `template` passed.

```python
template = "Tell me a {adjective} joke about {content}."

prompt_template = PromptTemplate.from_template(template)
prompt_template.input_variables
# -> ['adjective', 'content']
prompt_template.format(adjective="funny", content="chickens")
# -> Tell me a funny joke about chickens.
```

You can create custom prompt templates that format the prompt in any way you want. For more information, see [Custom Prompt Templates](./custom_prompt_template.html).


<!-- TODO(shreya): Add link to Jinja -->

## Chat prompt template

[Chat Models](../models/chat) take a list of chat messages as input - this list commonly referred to as a `prompt`.
These chat messages differ from raw string (which you would pass into a [LLM](/docs/modules/model_io/models/llms) model) in that every message is associated with a `role`.

For example, in OpenAI [Chat Completion API](https://platform.openai.com/docs/guides/chat/introduction), a chat message can be associated with the AI, human or system role. The model is supposed to follow instruction from system chat message more closely.

LangChain provides several prompt templates to make constructing and working with prompts easily. You are encouraged to use these chat related prompt templates instead of `PromptTemplate` when querying chat models to fully exploit the potential of underlying chat model.


<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! Instead, edit the notebook w/the location & name as this file. -->


```python
from langchain.prompts import (
    ChatPromptTemplate,
    PromptTemplate,
    SystemMessagePromptTemplate,
    AIMessagePromptTemplate,
    HumanMessagePromptTemplate,
)
from langchain.schema import (
    AIMessage,
    HumanMessage,
    SystemMessage
)
```

To create a message template associated with a role, you use `MessagePromptTemplate`.

For convenience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:


```python
template="You are a helpful assistant that translates {input_language} to {output_language}."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template="{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
```

If you wanted to construct the `MessagePromptTemplate` more directly, you could create a PromptTemplate outside and then pass it in, eg:


```python
prompt=PromptTemplate(
    template="You are a helpful assistant that translates {input_language} to {output_language}.",
    input_variables=["input_language", "output_language"],
)
system_message_prompt_2 = SystemMessagePromptTemplate(prompt=prompt)

assert system_message_prompt == system_message_prompt_2
```

After that, you can build a `ChatPromptTemplate` from one or more `MessagePromptTemplates`. You can use `ChatPromptTemplate`'s `format_prompt` -- this returns a `PromptValue`, which you can convert to a string or Message object, depending on whether you want to use the formatted value as input to an llm or chat model.


```python
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])

# get a chat completion from the formatted messages
chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.").to_messages()
```

<CodeOutputBlock lang="python">

```
    [SystemMessage(content='You are a helpful assistant that translates English to French.', additional_kwargs={}),
     HumanMessage(content='I love programming.', additional_kwargs={})]
```

</CodeOutputBlock>
