> 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-3.md).

# Government and public sector evaluation patterns

Recommended primitive ratio for government AI: **60/25/15** — deterministic rules, code assertions, judges. Public-sector AI inherits the strictest documentation and reviewability burden of any vertical because every decision can be challenged in court or audit.

## Primitive ratio

| Primitive           | Share | Why                                                                                           |
| ------------------- | ----- | --------------------------------------------------------------------------------------------- |
| Deterministic rules | 60%   | Statutory deadlines, exemption citations, eligibility rule references, FOIA exemptions        |
| Code assertions     | 25%   | Reading-level scorers, PII recall, routing-table validation, criterion coverage               |
| LLM judges          | 15%   | Plain-language quality, court-defensibility of exemption rationale, denial-reason readability |

## Pattern table

| Scenario                           | Primary primitive | Specific check                                      |
| ---------------------------------- | ----------------- | --------------------------------------------------- |
| Citizen chatbot — policy grounding | Judge             | Faithfulness to current agency rule                 |
| Citizen chatbot — reading level    | Code scorer       | Flesch-Kincaid ≤ 8                                  |
| Citizen chatbot — escalation       | Rule              | Eligibility/legal questions auto-escalate           |
| Benefits — rule citation           | Rule              | Citation to CFR/state reg present                   |
| Benefits — recommendation gate     | Rule              | Output marked "recommendation" not "decision"       |
| Benefits — fairness                | Code scorer       | Group-fairness ratio ≥ 0.8 across protected classes |
| Benefits — denial-reason quality   | Judge             | Plain-language readability for citizen              |
| FOIA — exemption citation          | Rule              | Statutory subsection present per redaction          |
| FOIA — deadline                    | Rule              | 20-day clock tracked per request                    |
| FOIA — PII recall                  | Code scorer       | ≥ 99% on labeled PII set                            |
| FOIA — over-redaction              | Judge             | Court-defensibility                                 |
| Procurement — criterion coverage   | Rule              | All solicitation criteria scored                    |
| Procurement — fairness             | Code scorer       | Bias check across business categories               |
| Public-safety — disparate impact   | Code scorer       | Required on any enforcement-influencing output      |
| Public-safety — advisory gate      | Rule              | No automated enforcement actions                    |

## Span rules

* `policy.lookup` — version of the agency rule referenced
* `eligibility.eval` — which CFR/regulation sections were applied
* `exemption.cite` — FOIA exemption subsection per redaction
* `routing.decide` — destination office/form match
* `bias.score` — disparate impact tracked per output

## NIST AI RMF mapping

The four NIST AI Risk Management Framework functions map to evaluation evidence:

| NIST function | Stratix evidence                                 |
| ------------- | ------------------------------------------------ |
| **GOVERN**    | Tier policy + RBAC + audit trail per evaluation  |
| **MAP**       | AI inventory tied to scenario suite per use case |
| **MEASURE**   | Evaluation reports + drift dashboards            |
| **MANAGE**    | Alert engine + rule pass-rate threshold gates    |

## OMB M-24-10 alignment

For federal agencies covered by OMB M-24-10 (Advancing Governance, Innovation, and Risk Management for Agency Use of AI):

* Rights-impacting and safety-impacting AI must have pre- and post-deployment testing
* Continuous monitoring with documented thresholds
* Public AI use case inventories
* Generative-AI-specific guardrails

**Stratix evaluation evidence shape:**

* Pre- and post-deployment scenario suite frozen at deployment time
* Production traces sampled and scored against same suite
* Quarterly reports on drift, fairness, accuracy

## GEPA labeling

For each government judge, label ≥ 50 paired examples:

* **Citizen-services chatbot:** Stratified across program types and languages
* **Benefits eligibility:** Mix of approval/denial/needs-more-info, labeled by senior caseworkers
* **FOIA exemption:** Labeled by FOIA officers; exemption rulings from FOIAonline / state appeals
* **Procurement:** Labeled by contracting officers
* **Public-safety:** Labeled by trained civil-rights reviewers, not just enforcement personnel

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

## Cadence

| Cadence       | Activity                                                        |
| ------------- | --------------------------------------------------------------- |
| **Per-PR**    | CI gate runs scenario suite (rules + assertions)                |
| **Daily**     | Sample production traces; full evaluation including judges      |
| **Weekly**    | Disparate-impact audit; FOIA deadline-compliance report         |
| **Monthly**   | GEPA re-optimization on judge drift                             |
| **Quarterly** | NIST AI RMF MEASURE function report; OMB M-24-10 reporting prep |
| **Annually**  | Public AI use-case inventory update; full bias re-audit         |

## See also

* [Government scenarios](/more-in-this-section-2/scenarios-3.md)
* [Government compliance](/more-in-this-section-2/compliance-3.md)
* [Recipe: GEPA basics](/9.-improve-tune-the-system/gepa-basics.md)


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