Prompt Templates

With Prompt Templates, you can seamlessly create and manage your LLM prompts in one place, and deploy them with just an API call. Prompt templates on Portkey are built to be production-ready - Portkey automatically tracks changes, maintains versions, and gives both the developer and the prompt engineer immense flexibility to do fast experimentation without breaking prod.

How to use Prompt Templates

• On the Portkey app, just click on the "Prompts" button on the left, click on "Create" and a new, blank playground opens up.

• Here you can pick your provider & model of choice - Portkey supports vision, chat, and completions models from 20+ providers. Provider choice here is tied up to Virtual keys so you may see multiple options for the same provider, based on the number of virtual keys you have.

• You can write the user/assistant messages as well as configure all the model parameters like top_p, max_tokens, logit_bias etc - right from UI.

• Portkey prompts also has support for enabling JSON Mode, and writing Tools/Functions call chains.

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Templating Engine

Portkey uses Mustache under the hood to power the prompt templates.

Mustache is a commonly used logic-less templating engine that follows a simple schema for defining variables and more.

With Mustache, prompt templates become even more extensible by letting you incorporate various {{tags}} in your prompt template and easily pass your data.

The most common usage of mustache templates is for {{variables}}, used to pass a value at runtime.

Using Variables

Let's look at the following template:

As you can see, {{customer_data}} and {{chat_query}} are defined as variables in the template and you can pass their value at the runtime:

import Portkey from 'portkey-ai'

const portkey = new Portkey()

const response = portkey.prompts.completions.create({
    promptID: "pp-hr-bot-5c8c6e",
    variables: {
        "customer_data":"",
        "chat_query":""
    }
})

Using variables is just the start! Portkey supports multiple Mustache tags that let you extend the template functionality:

Supported Tags

Tag	Functionality	Example
{{variable}}	Variable	Template: Hi! My name is {{name}}. I work at {{company}}. Data: { "name": "Chris", "company": "GitHub" } Output: Hi! My name is Chris. I work at Github.
{{#variable}} <string> {{/variable}}	Render <string> only if variable is true or non Empty	Template: Hello I am Tesla bot. {{#chat_mode_pleasant}} Excited to chat with you! {{/chat_mode_pleasant}} What can I help you with? Data: { "chat_mode_pleasant": False } Output: Hello I am Tesla bot. What can I help you with?
{{^variable}} <string> {{/variable}}	Render <string> only if variable is false or empty	Template: Hello I am Tesla bot. {{^chat_mode_pleasant}} Excited to chat with you! {{/chat_mode_pleasant}} What can I help you with? Data: { "chat_mode_pleasant": False } Output: Hello I am Tesla bot. Excited to chat with you! What can I help you with?
{{#variable}} {{sub_variable}} {{/variable}}	Iteratively render all the values of sub_variable if variable is true or non Empty	Template: Give atomic symbols for the following: {{#variable}} - {{sub_variable}} {{/variable}} Data: { "variable": [ { "sub_variable": "Gold" }, { "sub_variable": "Carbon" }, { "sub_variable": "Zinc" } ] } Output: Give atomic symbols for the following: - Gold - Carbon - Zinc
{{! Comment}}	Comments that are ignored	Template: Hello I am Tesla bot. {{! How do tags work?}} What can I help you with? Data: {} Output: Hello I am Tesla bot. What can I help you with?
{{>Partials}}	"Mini-templates" that can be called at runtime. On Portkey, you can save partials separately and call them in your prompt templates by typing {{>	Template: Hello I am Tesla bot. {{>pp-tesla-template}} What can I help you with? Data inpp-tesla-template: Take the context from {{context}}. And answer user questions. Output: Hello I am Tesla bot. Take the context from {{context}}. And answer user questions. What can I help you with?
{{>>Partial Variables}}	Pass your privately saved partials to Portkey by creating tags with double >> Like: {{>> }} This is helpful if you do not want to save your partials with Portkey but are maintaining them elsewhere	Template: Hello I am Tesla bot. {{>>My Private Partial}} What can I help you with?

Using Tags

You can directly pass your data object containing all the variable/tags info (in JSON) to Portkey's prompts.completions method with the variables property.

For example, here's a prompt partial containing the key instructions for an AI support bot:

And the prompt template uses the partial like this:

We can pass the data object inside the variables:

import Portkey from 'portkey-ai'

const portkey = new Portkey({})

const data = {
    "company": "NESTLE",
    "product": "MAGGI",
    "benefits": "HEALTH",
    "phone number": "123456",
    "name": "Sheila",
    "device": "iOS",
    "query": "Product related",
    "test_variable":"Something unrelated" // Your data object can also contain unrelated variables
}

// Make the prompt creation call with the variables
const response = portkey.prompts.completions.create({
    promptID: "pp-system-pro-34a60b",
    variables: {
        ...data,
        "user_query": "I ate Maggi and I think it was stale."
    }
})

console.log(response)

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Versioning Prompts

Whenever any changes are made to your prompt template, Portkey saves your changes in the browser but they are not pushed to Portkey. You can click on the Update button on the top right to save the latest version of the prompt on Portkey.

All of your prompt versions can be seen on the right column of the playground:

You can Restore or Publish any of the previous versions by clicking on the elipsis.

Publishing Prompts

Updating the Prompt does not automatically update your prompt in production. While updating, you can tick Publish prompt changes which will also update your prompt deployment to the latest version.