AI-generated product descriptions have a reputation for being obvious. Generic. Soulless.
That's because most people use AI wrong.
I built a system for CR Deals Cincinnati that generates 50-100 product listings daily. The descriptions don't sound like AI. They sound like someone who actually looked at the product and understood what matters to buyers.
Here's how.
Why Most AI Descriptions Fail
Generic prompts produce generic results.
If you tell ChatGPT "write a product description for a blue chair," you get a description that could apply to any blue chair ever made.
No context about your brand voice.
AI doesn't know if you're casual or formal, technical or approachable, luxury or budget.
No specific product details.
AI can't see your product. It's guessing based on minimal information.
What Actually Works
1. Feed the AI Real Product Data
For CR Deals, we built a pipeline that:
- Analyzes product photos with vision AI
- Extracts brand, model, condition, features
- Identifies key selling points
The AI isn't guessing. It's working from actual data about the specific product.
2. Train on Your Existing Descriptions
Before generating anything new, we analyzed hundreds of CR Deals' best-performing listings. What made them work? What language did they use?
Then we built prompts that replicated that style.
3. Include Category-Specific Templates
Different products need different approaches:
- Electronics need specs and compatibility info
- Furniture needs dimensions and materials
- Collectibles need condition details and provenance
The system routes products to category-specific prompts.
4. Quality Control in the Pipeline
AI makes mistakes. The system includes:
- Automated checks for obvious errors
- Flagging for human review when confidence is low
- Easy editing workflow for quick fixes
The Results
For CR Deals:
- $52K annual savings - No more manual listing creation
- 1,040+ hours back - Team focuses on sourcing, not typing
- 50-100 products listed daily - Impossible with manual process
- Paid for itself in the first month
When to Use AI for Product Descriptions
Good fit:
- High volume (50+ products/week)
- Relatively similar product types
- Descriptions follow a pattern
- Time savings outweigh quality concerns
Bad fit:
- Low volume (AI setup cost doesn't make sense)
- Highly unique products requiring expertise
- Premium positioning where handcrafted copy matters
Getting Started
You don't need a fully automated pipeline to start. Begin with:
- Collect 20-30 of your best product descriptions
- Identify what makes them work
- Build a prompt template that captures your style
- Test on new products
- Iterate until quality matches human-written
If you're drowning in product listing work and want AI to handle it, book a call. I'll help you figure out if AI is the right solution and what it would take to build.
Check out my AI automation services to see what's possible.