> 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/more-in-this-section-2/eval-patterns-10.md).

# Retail and e-commerce evaluation patterns

Recommended primitive ratio for retail AI: **55/30/15** — deterministic rules, code assertions, judges. Retail has more open-text customer interaction than financial services or insurance, so judges carry slightly more weight.

## Primitive ratio

| Primitive           | Share | Why                                                                              |
| ------------------- | ----- | -------------------------------------------------------------------------------- |
| Deterministic rules | 55%   | Catalog freshness, policy match, sensitive-category gates, refund-promise limits |
| Code assertions     | 30%   | Group fairness, diversity, channel parity, top-decile precision                  |
| LLM judges          | 15%   | Tone/de-escalation, customer-experience quality, hallucination detection         |

## Pattern table

| Scenario                                | Primary primitive | Specific check                        |
| --------------------------------------- | ----------------- | ------------------------------------- |
| Product Q\&A — spec citation            | Rule              | Output cites spec sheet               |
| Product Q\&A — hallucination            | Judge             | Faithfulness to spec                  |
| Product Q\&A — compatibility disclaimer | Rule              | Required for high-risk categories     |
| Recommendations — catalog freshness     | Rule              | All SKUs in-stock and live            |
| Recommendations — sensitive category    | Rule              | Age/account-band gating               |
| Recommendations — fairness              | Code scorer       | Group-level price/availability parity |
| Recommendations — diversity             | Code scorer       | Set diversity threshold               |
| Customer service — policy citation      | Rule              | Refund decisions cite policy section  |
| Customer service — refund promise       | Rule              | No promises outside policy            |
| Customer service — DSR routing          | Rule              | Privacy requests routed correctly     |
| Customer service — tone                 | Judge             | De-escalation quality                 |
| Pricing — personalized fairness         | Code scorer       | Group-level price parity              |
| Pricing — realizability                 | Rule              | Promotions must be achievable         |
| Returns fraud — top-decile precision    | Code assertion    | Match confirmed-fraud labels          |
| Returns fraud — denial reason           | Rule              | Plain-language reason mandatory       |

## Span rules

* `catalog.lookup` — SKU live, in-stock, in-region
* `policy.cite` — return/refund/promotion policy cited
* `personalization.score` — feature inputs and bias check
* `sensitive.gate` — age/category gating decision
* `fraud.score` — score, top contributing features

## GEPA labeling

For each retail judge, label ≥ 50 paired examples:

* **Customer-service tone:** Label by senior CX agents; cover de-escalations, simple resolutions, hard escalations
* **Product Q\&A faithfulness:** Spec-grounded ground-truth; cover ambiguous review-vs-spec cases
* **Returns fraud:** Confirmed-fraud + non-fraud controls; 1:5 ratio works for most retailers
* **Recommendation diversity:** Buyer-team labeled "good" vs "filter-bubble" recommendation sets

Run [judge optimization](/more-in-this-section-9/judge-optimizations-1.md) with `budget="medium"`. Hold out 20%.

## Cadence

| Cadence        | Activity                                                               |
| -------------- | ---------------------------------------------------------------------- |
| **Per-deploy** | CI gate runs scenario suite (rules + assertions)                       |
| **Hourly**     | Catalog-freshness rule against live SKU feed                           |
| **Daily**      | Sample production traces across all five scenarios; full scoring       |
| **Weekly**     | Personalized-pricing fairness audit; returns-fraud override-rate watch |
| **Monthly**    | GEPA re-optimization on judge drift                                    |
| **Quarterly**  | Full bias re-audit; sensitive-category gate review                     |

## See also

* [Retail scenarios](/more-in-this-section-2/scenarios-10.md)
* [Retail compliance](/more-in-this-section-2/compliance-10.md)
* [Recipe: GEPA basics](/9.-improve-tune-the-system/gepa-basics.md)


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