Evaluating prompt templates in deployment spaces
You can evaluate prompt templates in deployment spaces with the watsonx.governance service to measure the performance of foundation model tasks and understand how your model generates responses.
With watsonx.governance, you can evaluate prompt templates in deployment spaces to measure how effectively your foundation models generate responses for the following task types:
Prompt templates are saved prompt inputs for foundation models. You can evaluate prompt template deployments in pre-production and production spaces.
Before you begin
Required permissions
You must have the following roles to evaluate prompt templates:
Admin or Editor role in a deployment space
In your project, you must also create and save a prompt template and promote a prompt template to a deployment space. You must specify at least one variable when you create prompt templates to enable evaluations.
The following sections describe how to evaluate prompt templates in deployment spaces and review your evaluation results:
Evaluating prompt templates in pre-production spaces
Run evaluation
To run prompt template evaluations, you can click Evaluate on the Evaluations tab when you open a deployment to open the Evaluate prompt template wizard. You can run evaluations only if you are assigned the Admin or Editor roles for your deployment space.
If you don't have a watsonx.governance instance that is associated with your deployment space, you must select Associate a service instance in the Associate a service instance dialog box before you can run evaluations. In the Associate instance for evaluation window, you must choose the watsonx.governance instance that you want to use and select Associate a service instance to associate an instance with your deployment space. You must be assigned the Admin role for your deployment space to associate instances.
If you don't have a database that is associated with your watsonx.governance instance, you must also associate a database before you can run evaluations. To associate a database, you must also click Associate database in the Database required dialog box to connect to a database. You must be assigned the Admin role for your deployment space and watsonx.governance instance to associate databases.
Select dimensions
The Evaluate prompt template wizard displays the dimensions that are available to evaluate for the task type that is associated with your prompt. You can expand the dimensions to view the list of metrics that are used to evaluate the dimensions that you select.
Watsonx.governance automatically configures evaluations for each dimension with default settings. To configure evaluations with different settings, you can select Advanced settings to set minimum sample sizes and threshold values for each metric as shown in the following example:
Select test data
You must upload a CSV file that contains test data with reference columns and columns for each prompt variable. When the upload completes, you must also map prompt variables to the associated columns from your test data.
Review and evaluate
You can review the selections for the prompt task type, the uploaded test data, and the type of evaluation that runs. You must select Evaluate to run the evaluation.
Reviewing evaluation results
When your evaluation finishes, you can review a summary of your evaluation results on the Evaluations tab in watsonx.governance to gain insights about your model performance. The summary provides an overview of metric scores and violations of default score thresholds for your prompt template evaluations.
To analyze results, you can click the arrow 
next to your prompt template evaluation to view data visualizations of your results over time. You can also analyze results from the model
health evaluation that is run by default during prompt template evaluations to understand how efficiently your model processes your data.
The Actions menu also provides the following options to help you analyze your results:
- Evaluate now: Run evaluation with a different test data set
- All evaluations: Display a history of your evaluations to understand how your results change over time.
- Configure monitors: Configure evaluation thresholds and sample sizes.
- View model information: View details about your model to understand how your deployment environment is set up.
If you track your prompt templates, you can review evaluation results to gain insights about your model performance throughout the AI lifecycle.
Evaluating prompt templates in production spaces
Activate evaluation
To run prompt template evaluations, you can click Activate on the Evaluations tab when you open a deployment to open the Evaluate prompt template wizard.
If you don't have a watsonx.governance instance that is associated with your deployment space, you must select Associate a service instance in the Associate a service instance dialog box before you can run evaluations. In the Associate instance for evaluation window, you must choose the watsonx.governance instance that you want to use and select Associate a service instance to associate an instance with your deployment space. You must be assigned the Admin role for your deployment space to associate instances.
If you don't have a database that is associated with your watsonx.governance instance, you must also associate a database before you can run evaluations. To associate a database, you must also click Associate database in the Database required dialog box to connect to a database. You must be assigned the Admin role for your deployment space and watsonx.governance instance to associate databases.
Select dimensions
The Evaluate prompt template wizard displays the dimensions that are available to evaluate for the task type that is associated with your prompt. You can provide a label column name for the reference output that you specify in your feedback data. You can also expand the dimensions to view the list of metrics that are used to evaluate the dimensions that you select.
Watsonx.governance automatically configures evaluations for each dimension with default settings. To configure evaluations with different settings, you can select Advanced settings to set minimum sample sizes and threshold values for each metric as shown in the following example:
Review and evaluate
You can review the selections for the prompt task type and the type of evaluation that runs. You can also select View payload schema or View feedback schema to validate that your column names match the prompt variable names in the prompt template. You must select Activate to run the evaluation.
To generate evaluation results, select Evaluate now in the Actions menu to open the Import test data window when the evaluation summary page displays.
Import test data
In the Import test data window, you can select Upload payload data or Upload feedback data to upload a CSV file that contains labeled columns that match the columns in your payload and feedback schemas.
When your upload completes successfully, you can select Evaluate now to run your evaluation.
Reviewing evaluation results
When your evaluation finishes, you can review a summary of your evaluation results on the Evaluations tab in watsonx.governance to gain insights about your model performance. The summary provides an overview of metric scores and violations of default score thresholds for your prompt template evaluations.
To analyze results, you can click the arrow next to your prompt template evaluation to view data visualizations of your results over time. You can also analyze results from the model
health evaluation that is run by default during prompt template evaluations to understand how efficiently your model processes your data.
The Actions menu also provides the following options to help you analyze your results:
- Evaluate now: Run evaluation with a different test data set
- Configure monitors: Configure evaluation thresholds and sample sizes.
- View model information: View details about your model to understand how your deployment environment is set up.
If you track your prompt templates, you can review evaluation results to gain insights about your model performance throughout the AI lifecycle.