Prompt Engineering in Production: Optimizing E-commerce Copy
Many developers treat prompts as magic spells—they tweak words randomly until it “feels right.” This case study shows how we applied engineering rigor to prompt design for an e-commerce platform.
The Challenge: Generic Descriptions
We needed to generate SEO-friendly descriptions for 50,000 products.
- Initial Prompt: “Write a description for this product.”
- Result: Generic, repetitive fluff. “This high-quality widget is perfect for your needs.”
The Solution: Systematic Iteration
We moved through three phases of optimization.
Phase 1: Role & Constraints (System Prompt)
We gave the model a persona.
“You are a senior copywriter for a luxury lifestyle brand. Use active voice. Avoid clichés like ‘game-changer’ or ‘must-have’.”
Phase 2: Few-Shot Prompting
We provided 3 examples of “Golden Descriptions” that our best human writers had created. This taught the model the style implicitly.
Phase 3: Chain-of-Thought
We asked the model to identify the Unique Selling Point (USP) first, and then write the copy based on that USP.
Results
| Metric | Zero-Shot | Few-Shot + CoT |
|---|---|---|
| SEO Ranking | Page 3 | Page 1 |
| Click-Through Rate | 2.1% | 2.4% (+15%) |
| Hallucinations | Frequent | Rare |
Conclusion
Prompt Engineering is not art; it’s engineering. By providing examples (Few-Shot) and structure (CoT), you can turn a generic model into a specialized expert.
