System tools for Content Recommendations

3 min

System tools are built-in features that help Optimizely Opal take action. Each tool performs a specific task, such as creating a campaign, uploading files, or generating images. Think of tools like attachments on a Swiss Army knife. Each one has a distinct purpose that helps you get work done.

In addition to the system tools available in Opal, Optimizely Content Recommendations includes a set of system tools designed to help you find high-performing content items and topics and analyze recommendation widget performances.

Note

Ask Opal what tools it has at any time. For example, enter "Please list the tools you have with a brief description of what they do and the parameters" into Opal Chat.

Click a tool's name to expand it and learn when to use it, its required and optional parameters, and example prompts for calling the tool. If you do not provide a required parameter, Opal prompts you for it.

To enable the Content Recommendations tools, go to Connections > Content Recommendations and select one or more product instances. When you enter a prompt in Chat, select the instance from the Set Product Instance drop-down list to get data for that instance. You can access data for the past 30 days.

Note

If you use Opti ID, administrators can turn off generative AI in the Opti ID Admin Center. See Turn generative AI off across Optimizely applicationsTurn generative AI off across Optimizely applications.

System tools for Content Recommendations

contentrecs_top_content– Retrieves the highest-performing content items ranked by user interactions within a specified date range. Helps identify which articles, posts, or content pieces resonate most with your audience

contentrecs_top_topics – Retrieves performance metrics for the most engaging topics based on user interactions. Identifies trending topics and reveals which topics resonate most with your audience.

contentrecs_top_recommendation_widgets – Analyzes recommendation widget performance with detailed A/B testing metrics comparing personalized and unpersonalized recommendations.