Video tutorials
Overview
Intent Libraries allow you to build dynamic knowledge bases of real-world user queries. Instead of guessing what your customers are searching for, this feature lets you scrape data from external sources (like Reddit communities, competitor websites, or your own search logs) to generate a library of high-value "user intents."
You can then run these real-world queries against your catalog to see if your products are discoverable by AI agents.
Managing Knowledge Sources
To create an intent library, you must first define where the data should come from.
Access Sources: From the Intent Libraries dashboard, click the Manage Sources button in the top right.
Add a New Source: Click + Add Source. You can choose from three source types:
Website: Scrape any URL, such as a competitor's site, a niche blog, or your own storefront.
CSV Upload: Upload raw data files, such as a Google Search Console export containing your existing search terms.
Subreddit: Scrape discussions from Reddit to understand how real people talk about products in your niche.
Configuring a Subreddit Source
When adding a subreddit (e.g., r/snowboarding), it is critical to provide context so the AI filters out irrelevant noise.
Subreddit URL: Paste the link to the specific community.
Scraping Guidance: Use this field to tell the AI exactly what topics to extract.
Example: "I want to know what people are asking about snowboard equipment, specifically boots and bindings."
Note: Without guidance, a general subreddit might return irrelevant posts about weather or travel plans.
Generating an Intent Library
Once your sources are defined (e.g., a subreddit and a competitor website), you combine them to generate actionable prompts.
Create Library: Click + Generate Library on the main dashboard.
Select Sources: Check the boxes next to the sources you want to analyze (e.g., "Snowboard sub reddit" and "Competitor Website").
Define Context:
Library Name: Give your collection a descriptive title.
Additional Instructions: Provide high-level instructions to guide the LLM on what kind of prompts to generate.
Example: "Looking for purchase intents related to boots, bindings, and snowboards."
Generate: Click Generate Prompts. The system will analyze the source data and synthesize a list of potential user queries.
Reviewing and Filtering Prompts
After generation is complete, click on your new library to view the results. The system organizes the raw data into structured, searchable prompts.
Categorization: The tool automatically tags prompts into Categories (e.g., "Snowboard Boots") and Sub-intents (e.g., "by lacing system," "by stiffness").
Filtering: Use the dropdown menus at the top to drill down. For example, you can filter specifically for Snowboard Boots -> By Lacing System to see queries related to BOA vs. traditional laces.
Execution: Testing Against the Catalog
The final step is to verify how your products perform against these real-world queries.
Select a Prompt: Find a relevant query in your library (e.g., "Show me stiff bindings for fast carving").
Run Search: You can copy this text and navigate to the Global Catalog Browser.
Analyze Results: Paste the prompt into the Custom Search bar to see if your store appears in the AI's top recommendations or if you are "Unlisted."