> ## Documentation Index
> Fetch the complete documentation index at: https://portkey-docs-mintlify-add-terraform-provider-docs-72855.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Prompt Render

> Renders a prompt template with its variable values filled in


Given a prompt ID, variable values, and *optionally* any hyperparameters, this API returns a JSON object containing the **raw prompt template**.

<Note>
  Note: Unlike inference requests, Prompt Render API calls are processed through Portkey's Control Plane services.
</Note>

<Accordion icon="lightbulb" title="Example: Using Prompt Render output in a new request">
  Here’s how you can take the output from the `render API` and use it for making a separate LLM call. We’ll take example of OpenAI SDKs, but you can use it simlarly for any other frameworks like Langchain etc. as well.

  <CodeGroup>
    ```py OpenAI Python theme={null}
    from portkey_ai import Portkey
    from openai import OpenAI

    # Retrieving the Prompt from Portkey

    portkey = Portkey(
      api_key="PORTKEY_API_KEY"
    )

    render_response = portkey.prompts.render(
      prompt_id="PROMPT_ID",
      variables={ "movie":"Dune 2" }
    )

    PROMPT_TEMPLATE = render_response.data

    # Making a Call to OpenAI with the Retrieved Prompt

    openai = OpenAI(
        api_key = "OPENAI_API_KEY",
        base_url = "https://api.portkey.ai/v1",
        default_headers = {
          'x-portkey-provider': 'openai',
          'x-portkey-api-key': 'PORTKEY_API_KEY',
          'Content-Type': 'application/json',
        }
    )

    chat_complete = openai.chat.completions.create(**PROMPT_TEMPLATE)

    print(chat_complete.choices[0].message.content)
    ```

    ```ts OpenAI NodeJS theme={null}
    import Portkey from 'portkey-ai';
    import OpenAI from 'openai';

    // Retrieving the Prompt from Portkey

    const portkey = new Portkey({
        apiKey: "PORTKEY_API_KEY"
    })

    async function getPromptTemplate() {
        const render_response = await portkey.prompts.render({
            promptID: "PROMPT_ID",
            variables: { "movie":"Dune 2" }
        })
        return render_response.data;
    }

    // Making a Call to OpenAI with the Retrieved Prompt

    const openai = new OpenAI({
        apiKey: 'OPENAI_API_KEY',
        baseURL: 'https://api.portkey.ai/v1',
        defaultHeaders: {
          'x-portkey-provider': 'openai',
          'x-portkey-api-key': 'PORTKEY_API_KEY',
          'Content-Type': 'application/json',
        }
    });

    async function main() {
        const PROMPT_TEMPLATE = await getPromptTemplate();
        const chatCompletion = await openai.chat.completions.create(PROMPT_TEMPLATE);
        console.log(chatCompletion.choices[0]);
    }

    main();
    ```
  </CodeGroup>
</Accordion>