Prompts and Requests Library

Effective prompts play a crucial role in enhancing response accuracy when interacting with LLMs. The new Prompts Library module empowers bot designers by allowing them to create and test suitable prompts for their specific use cases. It also displays all the newly added/custom and default request/prompt templates for the integrated models with their status as active or inactive. The default prompts are related to the pre-built models. However, you can import any default prompt, customize it, and use it for a pre-built and custom LLM model.

The post processor for prompts allows designers to align LLM responses perfectly with Platform expectations. Designers can modify the LLM response to guarantee the correct behavior and high-quality interactions during runtime.

For more information on adding prompt for Agent Node, see Agent Node.

Note

Currently, the custom LLM integration and prompt creation are available only in English.

Pre-requisites

Before proceeding, ensure that you have successfully Integrated a Pre-built or Custom LLM. For more information, see LLM Integration.

How to add Prompts and Requests

To add a new prompt, follow the steps:

1. Go to Generative AI Tools > Prompts Library.

2. On the top right corner of the Prompts Library section, click + New Prompt.

3. Enter the prompt name. Select the feature and the respective model.

   

4. The Configuration section consists of End-point URLs, Authentication, and Header values required to connect to a large language model. These are auto-populated based on the input provided while model integration and are not editable.

   

5. In the Request section, you can either create a request from scratch or import the existing prompt from the Library to modify as needed.

   1. To import an existing prompt, do the following:

      1. Click Import from Prompts and Requests Library. All the newly added/custom prompts and prompts related to the pre-built LLM are displayed without integrating them.

         

      2. Select the Feature from the dropdown menu, select the Model, and select the Prompt from the dropdown menu. Hover over and click Preview Prompt to view the prompt before importing.

      Note

      You have the flexibility to interchange a prompt designated for one feature with that of another, and vice versa.

      1. Click Confirm to get it imported into the JSON body.

   2. To create a prompt from scratch, click Start from scratch and enter the JSON request the LLM.

6. (Optional) Toggle the Stream Response if required. If enabled, the response is sent to the user piece by piece as it’s being created in real time, instead of waiting for the entire response to be generated before displaying it. By default, the response streaming is disabled.
   

   Note

   • If enabled, ensure to add a stream parameter to the custom prompt, e.g., “stream”: true, to get the streaming response. The saved prompt will be displayed with the stream tag in the prompts library.
   • If enabled, the “exist scenario” field is disabled.
   • The response streaming applies only to the Agent Node and Prompt Node features using OpenAI and Azure OpenAI models.

7. Once you type the JSON, the Sample Context Values fields are displayed. Fill in the values and click Test.

8. If the request values are correct, the response from the LLM is displayed. If not, an error message is displayed.

   

9. Selecting a Key for Generating Response Path
   The XO Platform receives the LLM response in the JSON format, which contains multiple data fields. To display the relevant information from the JSON to the end user, select the key that holds the specific information. The XO Platform then generates a Response Path that identifies the exact location of the information within the JSON response.
   For example, double-click the "content" key in the JSON structure and click Save.
   

10. The Response Path is displayed. Click Lookup Path.
    

11. The Actual Response and Expected Response are displayed.

    1. If the response structure matches, the responses will be in green. Click Save. Skip to Step 11.

       Note: Both Actual Response and Expected Response are not editable.

    2. If the response structure does not match, the responses will be in red. Click Configure to modify the Actual Response. The Post Processor Script is displayed.

       1. Enter the Post Processor Script. Click Save & Test.
       2. The response is displayed.
       3. Click Save. The actual response and expected response turn green.

12. (Only for Agent Node) Enter the Exit Scenario Key-Value fields and Virtual Assistance Response Key. The Exit Scenario Key-Value fields help identify when to end the interaction with the GenAI model and return to the dialog flow. A Virtual Assistance Response Key is available in the response payload to display the VA’s response to the user.

13. Click Save. The request is added and displayed in the Prompts and Requests Library section.

14. Now proceed to enable Co-Pilot and Dynamic Conversations features.