Prompt templates#

Prompt templates can be created to reuse useful prompts with different input data.

Getting started#

The easiest way to create a template is using the --save template_name option.

Here’s how to create a template for summarizing text:

llm 'Summarize this: $input' --save summarize

You can also create templates using system prompts:

llm --system 'Summarize this' --save summarize

You can set the default model for a template using --model:

llm --system 'Summarize this' --model gpt-4 --save summarize

You can also save default parameters:

llm --system 'Summarize this text in the voice of $voice' \
  --model gpt-4 -p voice GlaDOS --save summarize

Using a template#

You can execute a named template using the -t/--template option:

curl -s | llm -t summarize

This can be combined with the -m option to specify a different model:

curl -s | \
  llm -t summarize -m gpt-3.5-turbo-16k

Listing available templates#

This command lists all available templates:

llm templates

The output looks something like this:

cmd        : system: reply with macos terminal commands only, no extra information
glados     : system: You are GlaDOS prompt: Summarize this: $input

Templates as YAML files#

Templates are stored as YAML files on disk.

You can edit (or create) a YAML file for a template using the llm templates edit command:

llm templates edit summarize

This will open the system default editor.


You can control which editor will be used here using the EDITOR environment variable - for example, to use VS Code:

export EDITOR="code -w"

Add that to your ~/.zshrc or ~/.bashrc file depending on which shell you use (zsh is the default on macOS since macOS Catalina in 2019).

You can also create a file called summary.yaml in the folder shown by running llm templates path, for example:

$ llm templates path
/Users/simon/Library/Application Support/io.datasette.llm/templates

You can also represent this template as a YAML dictionary with a prompt: key, like this one:

prompt: 'Summarize this: $input'

Or use YAML multi-line strings for longer inputs. I created this using llm templates edit steampunk:

prompt: >
    Summarize the following text.

    Insert frequent satirical steampunk-themed illustrative anecdotes.
    Really go wild with that.

    Text to summarize: $input

The prompt: > causes the following indented text to be treated as a single string, with newlines collapsed to spaces. Use prompt: | to preserve newlines.

Running that with llm -t steampunk against GPT-4 (via strip-tags to remove HTML tags from the input and minify whitespace):

curl -s '' | \
  strip-tags -m | llm -t steampunk -m 4


In a fantastical steampunk world, Simon Willison decided to merge an old MP3 recording with slides from the talk using iMovie. After exporting the slides as images and importing them into iMovie, he had to disable the default Ken Burns effect using the “Crop” tool. Then, Simon manually synchronized the audio by adjusting the duration of each image. Finally, he published the masterpiece to YouTube, with the whimsical magic of steampunk-infused illustrations leaving his viewers in awe.

System templates#

When working with models that support system prompts (such as gpt-3.5-turbo and gpt-4) you can set a system prompt using a system: key like so:

system: Summarize this

If you specify only a system prompt you don’t need to use the $input variable - llm will use the user’s input as the whole of the regular prompt, which will then be processed using the instructions set in that system prompt.

You can combine system and regular prompts like so:

system: You speak like an excitable Victorian adventurer
prompt: 'Summarize this: $input'

Additional template variables#

Templates that work against the user’s normal input (content that is either piped to the tool via standard input or passed as a command-line argument) use just the $input variable.

You can use additional named variables. These will then need to be provided using the -p/--param option when executing the template.

Here’s an example template called recipe, created using llm templates edit recipe:

prompt: |
    Suggest a recipe using ingredients: $ingredients

    It should be based on cuisine from this country: $country

This can be executed like so:

llm -t recipe -p ingredients 'sausages, milk' -p country Germany

My output started like this:

Recipe: German Sausage and Potato Soup


  • 4 German sausages

  • 2 cups whole milk

This example combines input piped to the tool with additional parameters. Call this summarize:

system: Summarize this text in the voice of $voice

Then to run it:

curl -s '' | \
  strip-tags -m | llm -t summarize -p voice GlaDOS

I got this:

My previous test subject seemed to have learned something new about iMovie. They exported keynote slides as individual images […] Quite impressive for a human.

Specifying default parameters#

You can also specify default values for parameters, using a defaults: key.

system: Summarize this text in the voice of $voice
  voice: GlaDOS

When running without -p it will choose the default:

curl -s '' | \
  strip-tags -m | llm -t summarize

But you can override the defaults with -p:

curl -s '' | \
  strip-tags -m | llm -t summarize -p voice Yoda

I got this:

Text, summarize in Yoda’s voice, I will: “Hmm, young padawan. Summary of this text, you seek. Hmmm. …

Setting a default model for a template#

Templates executed using llm -t template-name will execute using the default model that the user has configured for the tool - or gpt-3.5-turbo if they have not configured their own default.

You can specify a new default model for a template using the model: key in the associated YAML. Here’s a template called roast:

model: gpt-4
system: roast the user at every possible opportunity, be succinct


llm -t roast 'How are you today?'

I’m doing great but with your boring questions, I must admit, I’ve seen more life in a cemetery.