Reviewing AI-Generated Content

After you run enrichment, your results appear directly in the Products table. This is where you assess whether the AI-generated values are accurate, consistent, and ready for use. You don't need to check every single product—a smart spot-check approach is faster and more effective.

Where Your Results Live

Once enrichment completes, head back to your Products view. The attributes you just enriched now show values in their respective columns.

[SCREENSHOT: Products table with newly populated attribute columns showing AI-generated values]

The Review Workflow

Your enrichment results live in the same Products table where you work every day. This means:

• No separate review screen. You review directly in the table.
• Real-time feedback. As you review, you can immediately edit values (see Editing and Overriding AI Output for details).
• Easy to scale. Once you're confident in the quality, you can run enrichment on larger batches knowing your review process is simple.

Reviewing at Scale

If you've enriched hundreds or thousands of products, don't try to check every one. Instead, use these techniques to review efficiently:

Use Views to Filter Recently Enriched Products

Create a view that shows only the products you just enriched:

• Filter by a date range (products updated today)
• Filter by category or vendor to review similar products together
• Combine filters to create a focused sample

This narrows your review set to a manageable number.

[SCREENSHOT: Products view with filter applied showing only recently enriched products]

Sort by Specific Attributes

Click the column header of an attribute you just enriched to sort by that column. This groups similar values together, making patterns and outliers easier to spot.

[SCREENSHOT: Products table sorted by a newly enriched attribute, showing all values of the same type grouped together]

Scan Across Rows

Once you've filtered and sorted, scan horizontally across the table to see how each product's enriched data looks in context with other product information (name, category, price, etc.). This helps you judge whether the AI-generated value makes sense for that specific product.

What to Look For

When spot-checking, evaluate on three dimensions:

1. Accuracy

Does the value match the product?

• Example: If you enriched "Suggested Use Case" for a camping tent, does the AI suggest appropriate activities (camping, hiking) rather than unrelated uses?
• Red flag: Values that don't match the product's actual purpose or nature.

2. Format

Does the value follow the structure you asked for?

• Length: Is it the right length (one sentence, 3 bullet points, etc.)?
• Tone: Does it match your brand voice (professional, casual, technical)?
• Structure: If you asked for a specific format (e.g., "Price range: $X–$Y"), is it formatted correctly?
• Red flag: Values that are too long, too short, wrong tone, or don't follow your template.

3. Consistency

Do similar products have similar output patterns?

• Example: If you enriched "Size Range" for apparel, do all shirts have sizes like "XS–XXL," and all jackets also follow that same pattern?
• Red flag: One product says "Fits sizes 2–20," another says "Small to Extra Large," and a third says "All sizes." Same attribute, three different formats.

Spot-Checking Strategy

Rather than reviewing every product, pick a representative sample:

1. Choose products across categories. If you sell apparel, home goods, and electronics, pick a few products from each category.
2. Sample different vendors. If you work with multiple suppliers, review products from 2–3 vendors.
3. Pick different price ranges. If your catalog spans budget to premium, include products from each tier.
4. Include edge cases. If some of your products are unusual (limited editions, custom items, very old stock), include one or two of those.

This approach catches issues across your product diversity without needing to review every row.

Tracing Back to Configuration

If output looks wrong, the issue usually lies in one of four places:

1. Prompt clarity. Your prompt might be too vague, contradictory, or missing context. Re-read it and imagine explaining it to a colleague—if it's unclear to you, the AI will struggle too.
2. Product data context. The AI might be missing the information it needs. Check that you've selected the right Product Data fields when configuring the attribute.
3. Image Source. If your attribute relies on product images, verify that the images are clear, relevant, and actually show the product (not just packaging or logos).
4. Acceptable Values (if set). If you've limited the AI to specific values, verify those values are appropriate for the actual products you're enriching.

To troubleshoot, go back to the Attributes page (Workspace → Attributes), find the attribute, and review its configuration. Once you've made changes, use Re-running Enrichment to test your improvements on a small batch before scaling.

Common Issues

Output is too generic or vague

• Example: "Description" shows "A product that serves its intended purpose" for all products.
• Likely cause: Your prompt is too broad or Product Data context is missing. Try adding more specific guidance (e.g., "Include the material, primary use, and key features") and ensure you've selected descriptive Product Data fields.

Values don't match the expected format

• Example: You asked for "Price: $X–$Y" but got "Ranging from X dollars to Y dollars."
• Likely cause: Your prompt template isn't strict enough, or Acceptable Values aren't set. Consider refining your prompt with an explicit example, or use Acceptable Values to enforce a specific pattern.

Some products have blank values while others don't

• Example: 50 products enriched successfully, but 20 are empty.
• Likely cause: The AI requires a dependent attribute to generate the value, and some of your products are missing that dependency. Check your attribute configuration and verify all required upstream attributes have values.

Values are inconsistent across similar products

• Example: One "Fabric Type" says "Cotton," another says "100% cotton," another says "Natural fiber—cotton."
• Likely cause: Your prompt doesn't enforce consistent formatting, or Acceptable Values aren't used. Consider using a strict Acceptable Values list (Cotton, Polyester, Wool, Blend) to ensure uniformity.

Next Steps

1. Fix individual values: If you spot errors in specific cells, head to Editing and Overriding AI Output to correct them.
2. Improve your prompt: If you see systematic issues, update your attribute configuration and then check out Re-running Enrichment to re-generate with the updated logic.
3. Scale up: Once you're confident in quality, run enrichment on larger batches using the same approach.