> For the complete documentation index, see [llms.txt](https://docs.promptspace.app/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.promptspace.app/psl-docs/priming-a-model.md).

# Priming a model

LLM models like `text-davinci-003` simply take a message and generate text based on the message. On the other hand, chat completion models like `gpt-4` takes a chat history between user and assistant as input and generate a completion for that chat. PSL allows prompt engineers to specify the chat history via the `priming` attribute supported by all of our chat completion models. Primping is optional and specified in the following format:<br>

```ini
priming.0.user = <First user input>
priming.0.assistant = <First assistant response>
priming.1.user = <Second user input>
priming.1.assistant = <Second assistant response>
...
```

#### Example 1

Here we prime the `gpt-4` to output in a specified format (INI in this case). Priming can be very useful in making the model understand the output format if the desirable output format is structured.

```ini
[ask.title]
description = Please provide a title for your story book:

[prompt.generate_topic]
model_name = gpt-4
priming.0.user = I will provide you with a title of a story and you are required to provide a topic and imager for that story. 
    The format is INI with section name `story` and two fields `topic` and `imagery`. 
    
    Say okay to continue.
priming.0.assistant = Okay.
priming.1.user =
    tile: Snow White
priming.1.assistant = [story]
    topic = Snow white goes to the beach
    imagery = Snow White on a beach playing with friends inside a sand castle.
message =
    title: {{input.title}}
display = False
output_type = ini
```