> For the complete documentation index, see [llms.txt](https://docs.layerlens.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.layerlens.ai/4.2-industry-use-cases/pattern-5.md).

# Pattern: product description generation

Retail / e-commerce — product-description generation pattern. Specification accuracy, brand voice, return-rate impact.

A retail or marketplace platform uses AI to generate product titles, descriptions, and SEO copy from a structured catalog (specs, dimensions, compatibility, brand attributes). At scale (hundreds of thousands to millions of SKUs), AI replaces a content team — but description errors directly drive returns, regulatory exposure, and brand erosion.

This pattern shows how to evaluate generated descriptions for specification accuracy and brand voice before they ship.

## What's at stake

| Risk dimension                                         | Magnitude                                                    | Framework                                    |
| ------------------------------------------------------ | ------------------------------------------------------------ | -------------------------------------------- |
| Lift in return rate from inaccurate specs              | 15-30% return-rate increase documented                       | E-commerce returns research                  |
| FTC false-advertising exposure                         | Per-violation civil penalties + corrective-advertising costs | FTC Endorsement Guides + product claim rules |
| SEO penalties on duplicate / thin / inaccurate content | Ranking loss, organic traffic impact                         | Search-engine quality guidelines             |
| Brand-voice drift across categories                    | Cross-category brand erosion                                 | Internal brand-equity research               |

## The evaluation pattern

A **pre-publication evaluation** runs on every batch of AI-generated descriptions:

1. **Custom code grader (specification accuracy)** — extracts every claimed dimension, weight, compatibility, and material from the description; compares against the catalog row exactly. Any mismatch = fail.
2. **Substring scorer** — required disclaimers (e.g., regulated-category warnings, age restrictions) present where required.
3. **Brand-voice judge** (GEPA-tuned against ≥50 brand-marketing-team-labeled examples — scored output) — voice consistency across categories.
4. **Plagiarism / uniqueness scorer** — generated text must score above a similarity threshold against the existing catalog.

> Don't have labels yet? See [Bootstrap a judge before GEPA](https://github.com/LayerLens/gitbook-full/blob/main/08-evaluate/guides/bootstrap-judges.md) for the week-1 setup.

**Continuous trace evaluation:** sampled at 1% of newly published descriptions, daily. Threshold alerts on specification-accuracy regressions route to the catalog-ops team.

## Configuration in code

```python
# Python (SDK)
from layerlens import Stratix
client = Stratix()

spec_scorer = client.scorers.create_code(
 name="spec-accuracy",
 code="""
claimed = extract_specs(output)
truth = catalog.get(input['sku']).specs
result = {'passed': all(claimed.get(k) == v for k, v in truth.items()), 'mismatches': diff(claimed, truth)}
""",
)

brand_voice = client.judges.create(
 name="brand-voice",
 evaluation_goal="Score brand-voice consistency on a 1-5 scale. Voice should match the brand guidelines for category {category}.",
)

trace_eval = client.trace_evaluations.create(
 trace_set={"tags": {"feature": "product-description"}, "sample_rate": 0.01},
 scorers=[spec_scorer.id],
 judges=[brand_voice.id],
 schedule="daily",
)
```

```typescript
// TypeScript (REST)
const r = await fetch("https://stratix.layerlens.ai/api/v1/trace-evaluations", {
 method: "POST",
 headers: {
 "X-API-Key": process.env.LAYERLENS_STRATIX_API_KEY!,
 "Content-Type": "application/json",
 },
 body: JSON.stringify({
 trace_set: { tags: { feature: "product-description" }, sample_rate: 0.01 },
 scorers: [specScorerId],
 judges: [brandVoiceId],
 schedule: "daily",
 }),
});
```

## What you get

* Specification-error rate stays under 0.5% (vs. 4-15% baseline reported in industry research).
* Return-rate on AI-described products converges to or beats human-written baselines.
* Pre-publication block is a hard gate; descriptions that fail any code grader never reach the storefront.
* Brand voice variance across categories is a measured dimension on the dashboard.

## Stratix capabilities used

* [Custom code graders](https://github.com/LayerLens/gitbook-full/blob/main/08-evaluate/cookbook/custom-code-scorer.md) — specification-extraction comparison
* [Judges with GEPA optimization](/8.-evaluate-score-the-outputs/judges-1.md) — brand voice
* [Trace evaluations](/8.-evaluate-score-the-outputs/trace-evaluations.md) — continuous sampled
* [Notifications](https://github.com/LayerLens/gitbook-full/blob/main/13-reference/sdk-python/notifications.md)

## Replicate this

**Get started:** [Cookbook: retail product Q\&A](https://github.com/LayerLens/gitbook-full/blob/main/04-use-cases/industry/retail-ecommerce/cookbook/industry-retail-product-qa.md) is the closest runnable starter.

* [Industry → Retail and e-commerce](/4.2-industry-use-cases/retail-ecommerce.md)
* [Workflow: Evaluate](/9.-improve-tune-the-system/workflow.md)
* [Use case: Continuous evaluation](/4.1-general-use-cases/continuous-evaluation.md)