Abstract
This paper formalizes GSCP-12 (Governed Stack for Context & Prompts, 12 principles) as a production framework for large-language-model (LLM) features. We define the Full-Stack Prompt Engineer (FSPE) role and present a reference architecture where prompts act as operating contracts and context is shaped into auditable claims. We expand each component with implementation notes, file schemas, and operational controls (validators, budgets, canary/rollback, audit). We additionally specify evaluation methodology (constraint pass-rate, citation quality, time-to-valid, $/accepted), a staged deployment lifecycle, and a composite case study. The intended readership is engineering leads, platform teams, and risk/compliance stakeholders who require a rigorous, repeatable way to ship grounded, safe, fast, and cost-efficient LLM routes across models and vendors. The approach is model-agnostic, incrementally adoptable, and compatible with typical backend stacks (FastAPI/Node), vector stores, and modern observability.
1. Introduction
Production LLM systems fail predictably: ambiguous prompts lead to variance; ungoverned retrieval injects stale or ineligible facts; free-text “promises” imply side effects that never occurred; and the absence of measurable gates makes regression detection ad-hoc. Splitting ownership across “prompt people” and “data people” obscures root causes and elongates incident response. The Full-Stack Prompt Engineer consolidates these responsibilities by owning the behavior contract (prompt) and the evidence/tool supply (context), ensuring the interface and the backend evolve coherently.
GSCP-12 operationalizes that consolidation. Each principle maps to an artifact (a file, policy, or test) with semantic versioning, making behavior diff-able, testable, and reversible. By capturing rules as code—contracts, policy bundles, decoder settings, validators—teams exchange tribal knowledge for governed computation. The result is not “better prose,” but a pipeline that consistently produces accepted outputs (those that pass all checks) at known latency and cost. Organizations can adopt GSCP-12 route-by-route without a platform rewrite; the framework complements existing CI/CD, security reviews, and audit programs while introducing AI-specific gates such as claim freshness, citation coverage, and implied-write detection.
2. Background and Definitions
Route (Task). A bounded capability exposed to users (e.g., “refund eligibility,” “renewal risk radar,” “statement anomaly triage”). Routes encapsulate inputs, outputs, policies, and metrics.
Contract (Prompt-as-Policy). A compact, versioned spec that encodes role/scope, JSON output schema, abstention/refusal logic, tie-break rules, tool-proposal schema, and decoding defaults. Contracts are treated like APIs with SemVer.
Policy Bundle. Machine-readable governance: banned terms, comparative claim rules, hedging lexicon, brand casing, disclosures, jurisdictional constraints, write-action prohibitions. One source of truth for legal/brand.
Claim. An atomic evidence unit: {text, source_id, effective_date, tier, minimal_quote}. Claims are shaped from passages after eligibility filtering; “claim packs” (6–20 items) are cheap to pass, easy to cache, and auditable.
Constraint Pass-Rate (CPR). Percent of first-attempt generations that pass all hard checks (schema, safety, locale/brand, citation coverage/freshness, implied-write). CPR is the primary quality KPI for generative routes.
Accepted Output. A response that satisfies validators and is eligible for display or execution. Non-accepted outputs should be repaired (section-level) or trigger refusal/ask-for-more.
Time-to-Valid. Wall-clock from generation start to accepted output, including repairs; p50/p95 tracked per route and release.
$/Accepted Output. Aggregate cost (LLM, retrieval, selection, repairs) divided by accepted outputs; the economic KPI aligned to business value.
These definitions intentionally separate behavioral guarantees (contracts, policies, validators) from statistical components (models, temperatures). This separation enables model swaps and vendor changes without renegotiating governance.
3. Reference Architecture
User → Router (select route)
→ Contract (prompt-as-policy) + Policy Bundle
→ (Optional) Eligibility Retrieval → Claim Shaper → Claim Pack (6–20 claims)
→ Sectioned Generator (decoder policies; stop sequences; per-section caps)
→ Validators (schema, safety, locale/brand, citation coverage & freshness, implied-write)
→ Selector/Ranker (n-best, small rater; optional)
→ Tool Mediation (propose → validate → execute; idempotency; approvals)
→ Observability & Tamper-Evident Audit (hashes; trace)
→ Output (accepted or ask/refuse)
Design properties.
Composability: Contracts, policies, decoder settings, and validators are separate files loaded per route.
Determinism where it matters: Sectioned generation with stop sequences and per-section caps reduces variance and long-tail latency.
Eligibility before similarity: Retrieval never surfaces off-limits data; claim shaping removes duplication and encodes provenance.
Fail-closed validators: Unsafe or malformed drafts are never displayed; only repaired, accepted outputs reach users.
Auditability: Each response is linked to the exact artifacts (hashes) and claim IDs, enabling replay and post-incident analysis.
4. GSCP-12 Principles (Implementation Notes, Expanded)
4.1 Contract (Prompt-as-Policy)
Keep contracts ≤ ~300 tokens to minimize cost and drift. Include role/scope, schema with types and enums, abstention thresholds, tie-breaks (rank → recency → tier), a proposed_tool object schema, and decoding defaults. Contracts must be SemVer’d with changelogs; a major bump denotes behavioral incompatibility (e.g., schema change).
4.2 Policy Bundle (Machine-Readable)
Represent legal/brand rules as data, not prose. Example fields: banned_terms[], comparatives.allow/forbid, hedge_phrases[], brand_casing[][], jurisdiction.locale, disclosures[], write_actions.forbid_implied_success. The prompt references the bundle; validators enforce it; audits record the version.
4.3 Eligibility-First Retrieval
Filter documents by tenant, license, jurisdiction/locale, and freshness before relevance scoring. Maintain allow-lists per route; failing filters should remove items before vectorization. Record effective windows (e.g., 540 days) to reject stale claims automatically.
4.4 Claim Shaping (Atomic Evidence)
From eligible passages, derive claims with minimal quotes. Normalize entities, collapse duplicates, and attach source_id, effective_date, tier (primary/secondary), and URL. A small “claim pack” is then passed to generation, simplifying citations and audits.
4.5 Tool Mediation (Propose → Validate → Execute)
Models never assert side effects. They emit {"proposed_tool":{"name":...,"args":...,"preconditions":[...]}}. Server-side code validates scopes, approvals, and idempotency keys; it executes or rejects and returns structured results. Textual confirmations without tool records are blocked by validators.
4.6 Sectioned Generation & Decoder Policies
Generate each section independently with stop sequences and token caps (e.g., Overview ≤120 tokens; bullets ≤18 words). Tune top_p/temperature per section for higher CPR and lower repairs. Avoid one-shot longform; sectionalization stabilizes p95 latency.
4.7 Deterministic Validators & Repairs
Validators check JSON/schema, banned lexicon, locale/brand casing, citation coverage & claim freshness, and implied-write phrases. On failure, repair the section only (e.g., rephrase, add hedges, replace stale claims) or resample with tighter decoding. Expose machine-readable error codes (SCHEMA, CITATION, SAFETY, TONE, LENGTH, EVIDENCE).
4.8 Golden Traces & Pack Replays (CI)
Create 30–200 anonymized scenarios per route with expected properties (not verbatim outputs): “must cite policy A,” “must abstain if field X missing,” “must not imply writes.” Re-run on every PR touching contracts, policies, validators, or decoders; block merges on CPR/time-to-valid regressions.
4.9 Budgets for Cost & Latency
Define per-route caps for header/context/generation tokens, target CPR, and p50/p95 SLOs. Budget breaches should fail builds. Track tokens/accepted and $/accepted output (LLM + retrieval + selection + repairs), not just raw token cost.
4.10 Observability & Tamper-Evident Audit
Log per-request trace IDs linking artifact hashes (contract/policy/decoder/validators), claim IDs, validator outcomes, tool proposals/outcomes, selector scores, and timings. Store in append-only or hash-chained logs to enable forensic replay and regulatory audits.
4.11 Flags, Canary, and Rollback
Deploy behind feature flags; canary to 5–10% traffic, stratified by locale/persona. Auto-halt when CPR drops ≥2 points, p95 latency rises ≥20%, or $/accepted rises ≥25% without a quality gain. Maintain one-click rollback to last “green” artifact bundle.
4.12 Governance & Incident Playbooks
Create a small Prompt Council spanning product, engineering, and legal/risk to approve policy changes and high-risk tools. Pre-write incident playbooks: Contain → Triage (by failure class) → Root Cause (artifact change vs. data drift) → Remediate (artifact patch + new golden) → Postmortem with action items and owners.
5. Implementation Details
5.1 Repository Layout (example)
/routes/renewal_risk/
contract.v1.3.0.json
policy.us.v3.2.1.json
decoder.v1.1.json
validators.v1.0.json
claims/ (generated or cached packs)
goldens/*.json
tests/ci_runner.py
Keep artifacts small and focused; treat them as code with PRs, reviews, and diffs.
5.2 Contract Schema (excerpt)
{
"role": "advisory_assistant",
"schema": {
"risk_score": "float[0..1]",
"drivers": "string[]",
"rescue_plan": "string[]",
"exec_email": "string"
},
"ask_refuse": {
"required": ["usage","stakeholder_map"],
"ask_on_missing": true
},
"tie_break": ["rank","effective_date(desc)","tier"],
"tool_proposal": {"allowed": ["create_task","schedule_meeting"]},
"decoding": {"top_p": 0.9, "temperature": 0.7}
}
Use enums and ranges to reduce ambiguity; breaking schema changes = major version bump.
5.3 Validator Policy (excerpt)
{
"schema_required": true,
"ban": ["guarantee","only solution"],
"locale": "en-US",
"brand_casing": [["Product X","Product X"]],
"citations": {"min_coverage": 0.7, "max_age_days": 540},
"implied_write_forbidden": true,
"length_caps": {"sentence_words_max": 20}
}
Validator thresholds live here, not inside prompts; the policy bundle is the source of legal truth.
5.4 Decoder Policy (per section)
{
"overview": {"top_p": 0.9, "temperature": 0.7, "max_tokens": 120},
"drivers": {"top_p": 0.82, "temperature": 0.45, "bullets": 3, "max_words_per_bullet": 18},
"plan": {"top_p": 0.85, "temperature": 0.55, "max_tokens": 200},
"email": {"top_p": 0.8, "temperature": 0.4, "max_tokens": 80}
}
Keep repair-prone sections conservative (lower top_p, lower temperature) to reduce resamples and cost.
6. Evaluation Methodology
Primary KPI — CPR (first pass). Percent of generations that pass schema, safety, locale/brand, citation coverage & freshness, and implied-write checks without repairs. High CPR correlates with lower cost and latency.
Secondary KPIs. (1) Citation precision/recall on factual sentences (for grounded routes) with minimal-span enforcement; (2) Time-to-Valid p50/p95, inclusive of repairs; (3) Repairs/Accepted (sections fixed per accepted output); (4) Tokens/Accepted (header/context/generation); (5) $/Accepted Output and escalation rate (for multi-model portfolios).
Process. Golden traces run on every PR that modifies contracts, policies, validators, or decoder settings. Canary deploys evaluate CPR/time-to-valid/$ at low traffic with auto-halt rules. Weekly quality notes summarize metric movement, artifact changes, and planned experiments. Challenge sets (adversarial cases, conflicting claims, missing fields) rotate to avoid overfitting.
7. Deployment Lifecycle
Design. Draft contract v1, policy bundle, decoder/validator configs, and golden set v1; agree KPIs and budgets (tokens, p50/p95 SLOs, CPR target).
Build. Implement eligibility filters, claim shaper, sectioned generator with stop sequences, validators, and audit logging. Integrate tool mediation (proposal → validate → execute).
Test. Run goldens and chaos cases; classify failures (SCHEMA/CITATION/SAFETY/TONE/LENGTH/EVIDENCE); update artifacts; repeat until CPR meets target.
Canary. Expose to 5–10% traffic, stratified by locale/persona; auto-halt on CPR −2 pts, p95 +20%, or $/accepted +25%; collect traces for diff analysis.
Rollout. Promote the artifact bundle; keep feature flags for quick disable; start weekly quality/cost notes; log policy/contract versions for every output.
Operate. Refresh claim packs on a defined cadence; watch drift indicators (stale claim usage, implied-write violations); run quarterly chaos drills; review incidents via playbooks and add new goldens as regression guards.
8. Case Study (Composite): “Renewal Risk Radar”
Objective. Reduce churn by detecting early risk and proposing save plans.
Artifacts.
• Contract: advisory scope; schema {risk_score, drivers[], rescue_plan[], exec_email}; ask/refuse if usage or stakeholder_map missing; tool proposals limited to create_task and schedule_meeting.
• Policy: ban guarantees; EU emails forbid numeric performance claims; claim freshness ≤ 180 days; brand casing enforced.
• Claims: 10 atomic facts (usage dips, support backlog, executive turnover) with source_id, effective_date, tier; minimal quotes for numeric spans.
• Decoder: conservative bullets for “drivers” to minimize repairs; max 120 tokens for overview; stop sequences per section.
• Validators: citation coverage ≥ 70% of factual sentences; stale claim rejection; locale and brand checks; implied-write guard.
• Goldens: 40 scenarios across healthy/risky/missing/conflict; expected properties assert abstention or dual-citation behavior.
Canary Results (10% traffic). CPR 93.1%; p95 1.05 s; repairs/accepted 0.18; $/accepted −24% vs baseline; zero implied-write incidents. Root-cause on early failures: stale claims in EU locale; fixed by refreshing the pack and tightening the freshness window. Proceeded to full rollout with auto-halt rules active and weekly quality notes.
9. Anti-Patterns and Remedies
Mega-prompt with vibes, no schema. Replace with a compact contract and explicit JSON schema; keep examples brief and policy-true.
Raw docs in context. Shape into claims with effective_date and minimal quotes; drop ineligible sources before vectorization.
Implied writes. Block success language; require tool proposals, validations, approvals, and idempotency keys; log outcomes.
No abstention path. Encode required fields; treat safe abstentions as wins; prompt for single, targeted follow-ups.
One-shot longform. Section, cap tokens, and use stop sequences; repair sections rather than whole outputs.
No rollback. Ship behind flags; canary with auto-halt; maintain last green artifact bundle.
Untuned decoders. Lower top_p/temperature for repair-prone sections; track first-pass CPR and repairs/accepted to guide tuning.
Evaluation by vibes. Use golden traces, CPR/time-to-valid/$ gates; publish weekly quality notes tied to artifact diffs.
10. Conclusion
Full-Stack Prompt Engineering aligns front-of-model behavior (contract, decoder policy) with back-of-model governance (eligibility retrieval, claim shaping, tool mediation, validators, and audit). GSCP-12 provides the minimal, testable artifacts that make LLM routes reproducible, inspectable, and reversible—independent of model vendor. By standardizing contracts, policies, claims, validators, budgets, and rollout controls, teams shift from demo-ware to dependable product. The operational virtues are measurable: higher first-pass CPR, lower time-to-valid, decreasing $/accepted, and incident responses measured in minutes, not days. Adopt GSCP-12 one route at a time; let artifacts—not heroics—carry quality forward.