Categorizing large datasets manually can be both time-consuming and prone to inconsistencies. By using AI-driven category tagging, you can ensure uniformity and accuracy across vast amounts of data.
Automating the categorization of products for an online store, ensuring consistent and accurate tags across thousands of listings.
Improving search and filtering functionality by accurately categorizing products.
Inventory Management
Streamlining inventory tracking by categorizing products, making it easier to manage stock levels and reorder products as needed.
Facilitating better organization of warehouse storage by categorizing items into specific groups.
Data Analysis
Enhancing data analysis by categorizing products, allowing for more insightful reporting and trend identification.
Improving marketing strategies by understanding product distribution across different categories.
Content Management
Efficiently organizing digital content libraries by categorizing items, making it easier to find and manage content.
Ensuring consistent tagging of multimedia content for better retrieval and usage.
Step-by-step instruction
Step 1. Set Input Dataset
Consider a dataset containing product listings that need to be categorized. Here is a sample dataset before categorization:\
{{line}}
Step 2. Define the AI Prompt
Prompt Example
Categorize the product in @Product_Name based on its description into one of the following categories: Footwear, Accessories, Electronics, Apparel, Fitness Equipment.
Why This Prompt Is Good
Clearly states the task (categorization) and the basis (product description).
Provides specific categories to choose from, ensuring consistency and relevance.
Helps the AI focus on the key aspects of each product description to determine the most appropriate category.
{{line}}
Step 3. Configure the Flow Designer
Add the input dataset to the flow designer.
Select the AI Column node from the tools panel and enter the prompt.
Start with a row-by-row execution to fine-tune your prompt.
Correct your prompt, regenerate any single row, or remove all previous results.
Once you satisfied with the prompt, apply the AI Column Node to all rows (it will be applied only for empty cells).
For very large datasets that are bigger than 10,000 rows, run the flow for runtime processing over the whole dataset. Be aware that it can be costly for a large amount of data.
{{line}}
Step 4. Get Final Result
Here is the dataset after using the AI Column Node to categorize the products:
