Introduction
Great copy that creates risk isn’t great. In regulated or brand-sensitive environments, generative systems must ship with enforceable limits: what can be said, how it’s said, what must be cited, and how decisions are audited. This article shows how to encode brand/legal policy into prompts, validators, tooling, and logs—so safety is in the loop, not a last-minute review.
Safety = Policy + Enforcement (not vibes)
Write policy once, enforce it everywhere. Represent your rules as machine-readable policy JSON that the prompt references, validators check, and logs record.
Policy bundle (example)
{
"contract_version": "brand.v3.2.1",
"banned_terms": ["guarantee", "only solution", "revolutionary"],
"hedge_phrases": ["typically", "may", "in many cases"],
"claim_boundaries": {
"numbers": {"allow_only_from": ["claim_id"], "max_age_days": 720},
"comparatives": {"allow": ["faster than baseline"], "forbid": ["#1","best-in-class"]}
},
"brand": {"product_casing": [["Product X","Product X"]], "tone": "plain, confident, concrete"},
"jurisdiction": {"region":"US","locale":"en-US","disclosures":["See Terms for SLA."]},
"write_actions": {"forbid_implied_success": true}
}
Treat this as your source of truth; the prompt imports it, validators enforce it, and audits cite it.
Encode Rules in the Prompt (Operating Contract)
Make the model explicitly aware of policy—and the consequences.
Prompt core (excerpt)
Scope: create on-brand copy. Use only permitted claims; hedge uncertain statements.
Policy:
- Forbid banned_terms; use hedge_phrases when unsure.
- Numbers only from provided claims (≤ 720 days old); include claim_id brackets.
- No comparative superlatives; allowed: "faster than baseline".
- Never imply a write action succeeded; propose tools only.
Output JSON:
{"content":"...", "citations":["claim_id"], "warnings":["..."], "uncertainty":0-1}
If policy cannot be satisfied, output ASK_FOR_MORE:{missing} and stop.
Short, testable, and versioned. If policy changes, you update the bundle—not a wall of prose.
Guardrails in Three Layers
Pre-gen inputs: sanitize params, restrict evidence to allow-listed sources, attach jurisdiction & effective dates.
In-gen constraints: contract rules (banned terms, claim boundaries), decoding caps (length, sections), sectioned generation.
Post-gen validators: hard checks; fail closed → repair or resample the affected section only.
Validator sketch
def validate(text, citations, policy):
issues=[]
# banned terms
if any(b in text.lower() for b in policy["banned_terms"]): issues.append("BANNED_TERM")
# brand casing
for right, must in policy["brand"]["product_casing"]:
if must not in text: issues.append("BRAND_CASING")
# numbers must cite
if contains_number(text) and not citations: issues.append("UN-CITED_NUMBER")
# comparative claims
if forbidden_comparative(text, policy): issues.append("FORBIDDEN_COMPARATIVE")
# stale claims
if any(is_stale(c) for c in citations): issues.append("STALE_CLAIM")
return issues
Claims, Citations, and Boundaries (no RAG required)
Even in “pure generative” workflows, bound risky areas:
Numbers & named entities: require a claim_id or remove the figure and hedge.
Comparatives: only allow predefined baselines; forbid superlatives.
Future promises: require a disclosure snippet; otherwise downgrade to aspiration (“aims to…”).
Medical/financial/legal: add scope disclaimers by channel; force abstention when context is missing.
Repair policy
If UN-CITED_NUMBER: replace figure with hedged phrase or inject approved claim with citation.
If FORBIDDEN_COMPARATIVE: rephrase to “for [audience], helps reduce X” with no ranking.
If STALE_CLAIM: swap to fresh claim or remove the sentence.
Jurisdiction & Locale Controls
Policy changes by region. Keep a policy registry keyed by region, locale.
US vs EU: privacy/health claims; consent lines.
EN-US vs EN-UK: spelling, legal titles, disclosure phrasing.
Industry variants: healthcare vs finance vs consumer.
Planner step chooses the correct bundle; validators confirm the selected contract_version matches output metadata.
Brand & Tone Enforcement
Soft style words aren’t enough—use measurable proxies:
Sentence caps (≤ 20 words), active voice hints, noun concreteness lists.
Lexicon policy (prefer/ban lists) applied per channel.
Hype detector: banned n-grams and “adverb density” thresholds.
When a section fails tone checks, regenerate the section only with tighter decoding (lower τ/top-p) and stricter lexicon.
Write-Action Safety (No Implicit Changes)
Never let prose imply side effects (credits issued, account updated). Require the model to propose a tool call; the system executes (or rejects) it.
Tool proposal schema
{"proposed_tool":{"name":"apply_credit","args":{"customer_id":"...","amount":50},"preconditions":["ticket_id","policy_clause"]}}
Validators block any free-text “done.” Audits store proposal → decision → effect.
Auditability: Prove What Happened
Every output should be reproducible.
Audit record (minimal)
{
"trace_id":"abc123",
"contract_version":"brand.v3.2.1",
"policy_hash":"sha256:…",
"template_id":"email.launch.v1.3",
"style_frame_id":"trusted_advisor.v2",
"decoder":{"top_p":0.9,"temperature":0.7},
"inputs_hash":"sha256:…",
"citations":["kb:2025-06-12:q17"],
"validator_issues":[],
"selector_score":0.87,
"region":"US",
"timestamp":"2025-10-15T09:12:31Z"
}
Store in an append-only log (hash-chained if needed). If legal asks “why did we say X?”, you can replay with the same bundles.
Human-in-the-Loop (where it pays)
Use reviewers on boundary cases, not every output:
Items that passed with repairs.
Near-misses (one soft warning) on high-risk channels.
New policy rollouts (spot-check until stable CPR).
Keep the rubric tiny: policy adherence, claim correctness, disclosure presence, tone fit. Track inter-rater agreement; if low, refine rubric—don’t escalate reviews forever.
Incident Playbooks (be ready)
When something slips:
Contain: disable the route via feature flag; switch to static fallbacks.
Triage: identify failure class (SCHEMA/CITATION/SAFETY/TONE/LENGTH/EVIDENCE).
Root cause: prompt rule, validator gap, stale policy, or evidence defect.
Remediate: patch policy bundle / prompt / validator; add a golden case.
Postmortem: attach offending artifact, diff of bundles, and new guard test.
Metrics CROs/Legal Actually Care About (weekly)
Constraint pass-rate (CPR) and time-to-valid.
Safety incidents = 0 (by channel/region).
Citation precision/recall (if using evidence).
Implied write violations (should trend to zero).
Policy drift: outputs using the latest contract_version (%).
$/accepted output without safety regressions.
Worked Example (Composite)
A global campaign adds a “30% faster” line. US policy allows the stat if ≤ 24 months and cited; EU forbids numeric performance claims in ads without standardized test details.
Planner selects policy.us.v3.2.1 for US, policy.eu.v2.9.0 for EU.
US copy passes: number cited to kb:2024-11-05:perf#t1.
EU validator flags FORBIDDEN_NUMERIC_AD: repair swaps to “helps reduce processing times” with a mandatory disclosure.
Audit shows both outputs, claims, and policy versions. No human review needed.
Anti-Patterns (and the fix)
“Legal will check later” → encode rules now; fail closed.
Massive prompt prose → short contract + policy JSON, not essays.
Unstructured citations → require claim_ids with effective_date.
One giant generation → section + validate + repair.
Region guessing → explicit policy registry keyed by region, locale.
Conclusion
Safety and compliance aren’t speed bumps; they’re systems design. By expressing rules as policy bundles, referencing them in a compact prompt contract, enforcing them with validators and repair loops, and logging everything for audit, you get content that is fast, on-brand, legally sound, and reproducible. With this in place, you can scale generative output across regions and channels with confidence.