Enrichment Quality: What 'Good' Looks Like

Your catalog is now enriched with AI-generated content. But how do you know if it's good? This article helps you set benchmarks, spot problems, and improve quality before you push enriched data to your sales channels.

Quality Benchmarks by Attribute Type

Different attribute types have different quality markers. Here's what to look for:

Product Descriptions

Signs of good quality:

• Length is appropriate for your channel (e.g., 150 words for marketplace listings, 300+ for your own site).
• Tone matches your brand voice (is it technical, casual, luxury, eco-focused?).
• Key details are included: materials, dimensions, use cases, benefits.
• No filler or generic text like "this product is great" without specifics.
• Grammar and spelling are correct.
• Reads naturally — not obviously machine-generated.

Signs of poor quality:

• Too short (5 words) or too long (1,000+ words) for the intended channel.
• Generic boilerplate: "This item is a high-quality product designed for everyday use."
• Hallucinated details not found in your source data.
• Missing key information (what material is it, how big is it?).
• Awkward phrasing or grammar errors.
• Reads like a tech manual when you need friendly, conversational tone.

[SCREENSHOT: Side-by-side comparison showing a poor description ("This is a chair. It has four legs. You can sit on it.") next to a good description ("Mid-century modern dining chair with solid walnut frame and hand-stitched upholstery. Pairs well with industrial tables. Available in navy, charcoal, and natural linen.")]

Categorical Values (Categories, Collections, Tags)

Signs of good quality:

• Every value matches your acceptable values list exactly.
• Values are consistent — similar products get the same tags.
• Values are specific, not vague ("Sofas" is better than "Furniture").
• The selection makes sense for discovery (if it's a "Bedroom Set," it's also correctly tagged "Bedroom").

Signs of poor quality:

• Values don't exist in your acceptable values list.
• Inconsistent tags across similar products ("Sofa," "Couch," "Sectional" for the same product type).
• Too broad or too vague ("Home" when you meant "Home Office Furniture").
• Missing obvious categories that should be filled in.

Numeric Values (Price, Stock, Dimensions, Weight)

Signs of good quality:

• Values are within plausible ranges for your product type.
• Units are correct and consistent (all weights in lbs, or all in kg — not mixed).
• Precision matches your needs (dimensions to 0.1", weight to 0.5 lbs).
• Values align with your source data (if your vendor says 48", the AI should say 48", not 50").

Signs of poor quality:

• Implausible values ("Chair height: 2,000 inches").
• Mismatched units or missing units entirely.
• Hallucinated numbers not in your source data.
• Rounding errors or inconsistency (one product says "24"" and the next says "2 feet" for the same measurement).

Images

Signs of good quality:

• Images are clear and well-lit.
• The product is the main focus (not a crowded shelf or busy room).
• Image dimensions match your channel requirements.
• Images are relevant to the product and accurate.
• No watermarks or logos from competitor sites.

Signs of poor quality:

• Blurry, dark, or low-resolution images.
• Wrong product entirely (wrong color, style, or item).
• Images are too small or don't meet your channel's minimum dimension requirements.
• Watermarked or copyrighted images that can't be used.
• Generic stock photos that don't match your product.

Consistency Across Similar Products

One of the biggest quality markers is consistency. Sample 10 products in the same category:

• Do they all have descriptions of roughly the same length?
• Are they all using the same tone of voice?
• Are category tags applied consistently (all sofas tagged "Sofas," not half tagged "Sofas" and half tagged "Seating")?
• Are numeric values using the same units and precision?

If you see wild variation, your prompts or acceptable values need tightening.

Before and After: The Impact of Refinement

Let's say your initial product descriptions were vague. Here's what improvement looks like:

Before (Poor Prompt, Generic Sources):

"This sofa is a comfortable piece of furniture for your living room. It comes in different colors and sizes. Great for relaxing."

After (Refined Prompt, Specific Sources Added):

"Contemporary mid-century sofa with solid hardwood frame, high-density foam cushioning, and stain-resistant fabric. 84" wide, perfect for large living rooms. Available in charcoal gray, navy, and natural linen. Pairs beautifully with modern or transitional decor. Ships fully assembled."

The difference: the second version includes specific dimensions, materials, available colors, care details, and styling guidance. This came from:

• A more detailed prompt that asked for materials, dimensions, and care tips.
• Adding the manufacturer's spec sheet as a source.
• Tightening acceptable values for colors so the AI picks from valid options.

How to Improve Your Enrichment Quality

If your quality isn't where you want it, take these steps in order:

1. Refine Your Prompts

Go to Workspace → Attributes and review your prompts for each attribute:

• Are you asking for the right details? (Example: "Include materials, dimensions, and care instructions" is much better than "write a description.")
• Are you specifying tone and style? ("Write in a friendly, conversational tone" vs. "Write professionally.")
• Are you setting length expectations? ("150–200 words" vs. open-ended).
• Are you pointing to specific sources? ("Use the manufacturer's spec sheet and customer reviews, not competitor descriptions.")

2. Add Better Sources

Go to Workspace → Attributes and check the Product Data context. Are you using all available sources?

• Manufacturer specs and datasheets?
• High-quality images (not just low-res thumbnails)?
• Customer reviews and testimonials?
• Vendor descriptions (though avoid just copying them)?

More sources = better context = better AI output.

3. Tighten Your Acceptable Values

For categorical attributes, your acceptable values list is the guardrail. If the list is too long, vague, or inconsistent, the AI will struggle:

• Remove near-duplicates ("Sofa," "Couch," "Sectional Sofa" — pick one canonical term).
• Be specific ("Women's Footwear" instead of just "Shoes").
• Add helpful descriptions next to each value so the AI knows when to use it.

4. Be More Specific About Product Data Context

In the attribute settings, the "Product Data" field lets you tell the AI what fields to look at. Instead of letting it use everything:

• Specify: "Use the 'Specifications' section only, not competitor reviews."
• Or: "Use the product images and the manufacturer's material list. Ignore reviews with fewer than 4 stars."

5. Test and Iterate

After making changes, use "Generate All Attributes" on a small batch (5–10 products) and review. Does the output improve? If so, scale to your full catalog. If not, refine further and test again.

[SCREENSHOT: Before/After side-by-side showing enrichment results, with annotations highlighting specific improvements like "Now includes material," "Tone is friendlier," "Dimensions are specific."]

Red Flags: When Output Starts to Fail

Watch for these signs that your enrichment is degrading:

• Hallucination: The AI is making up details not in your source data (e.g., claiming a product has features it doesn't).
• Repetition: Descriptions are getting copy-paste boilerplate across multiple products.
• Mismatches: Categorical values that don't match your acceptable values list.
• Dropout: Some products stop generating values entirely.

If you see these, pause enrichment and revisit your prompts, sources, and acceptable values. Small fixes often resolve them.

You've Hit the Core Onboarding Milestone

If you've configured your attributes, enriched your catalog, and reviewed the output — congratulations, you've hit the core onboarding milestone. Your catalog is now rich, structured, and ready for the next step.

From here, you have two paths:

Connect an Integration — If you imported your catalog from a platform like Shopify or WooCommerce, you can sync your enriched data back. Head to How Integrations Work in Merchkit in Section 4.

Add a Channel — Once your enrichment is solid, you can optimize content for specific sales channels (Amazon, Wayfair, your own site). Go to Section 5 to learn about channels and start generating channel-optimized listings.

Both paths are available to you now. Choose based on your immediate needs.