LLMs  

A Deep Dive into LLMs Using GSCP (Gödel’s Scaffolded Cognitive Prompting)

Executive Summary

Large Language Models (LLMs) are powerful but unreliable if left as “best-guess generators.” Gödel’s Scaffolded Cognitive Prompting (GSCP) turns an LLM into a governed reasoning system by layering intent clarification → evidence grounding → conflict detection → compliance validation → uncertainty handling → audit logging. This paper details the architecture, prompt patterns, evaluation methods, and deployment blueprint to operationalize GSCP in production—especially for regulated domains.

1) Why GSCP for LLMs?

Problem: LLMs hallucinate, drift with context, and lack traceability.

Goal: Produce accurate, reproducible, auditable outputs with controllable risk.

Approach: Replace single-shot prompts with a scaffolded pipeline that enforces checks and creates machine-readable evidence of due diligence.

2) GSCP Architecture at a Glance

Stages (synchronous pipeline):

  1. Pre-Validation (Intent & Boundaries): restate task, surface ambiguities, apply domain constraints.
  2. Evidence Grounding: retrieve or accept provided sources; bind the LLM to those sources only.
  3. Draft Generation: structured output with sectioned reasoning.
  4. Conflict & Consistency Checks: contradiction detection against sources and within the draft; optional self-consistency (n-best voting).
  5. Compliance & Safety Filters: PII/PHI redaction, policy/format checks, harmful content screens.
  6. Uncertainty & Escalation: mark “requires verification,” route to human when confidence < threshold.
  7. Audit & Telemetry: emit JSON artifacts (prompts, versions, citations, checks, scores) to an AI Compliance Ledger.

Optionally asynchronous: background re-validation, red-team probes, and re-scoring for high-stakes outputs.

3) Core Prompt Patterns (copy-ready)

3.1 Pre-Validation (Intent & Constraints)

You are a {role}. First, restate the user task in 1–2 lines.
List any ambiguities or missing data as bullets.
State the applicable constraints: {policies}, {format}, {domain_rules}.
Do NOT solve yet. Return only: {restatement, ambiguities, constraints}.

3.2 Evidence-Bound Generation

Use ONLY the EVIDENCE below. If needed info is absent, say "NOT FOUND".
Cite evidence as [S1], [S2], … matching the provided chunks.

EVIDENCE:
[S1] {chunk_1}
[S2] {chunk_2}
...

TASK: {task}
OUTPUT FORMAT: {sections / schema}
RULES: no external knowledge; flag uncertainties explicitly.

3.3 Conflict Detection (Self-Check)

Review your DRAFT. For each claim, list supporting citations [S#].
Flag any contradictions (within the draft or vs. evidence).
Rewrite the DRAFT to remove unsupported claims; mark remaining gaps as "REQUIRES VERIFICATION".
Return: {final_output, contradictions_fixed[], still_uncertain[]}

Apply policy checks: {HIPAA/GDPR/OrgPolicy IDs}.
Redact PII/PHI; ensure required disclaimers and formatting.
If policy cannot be satisfied, stop and return: "BLOCKED: {reason}".

3.5 Uncertainty Handling

Assign confidence per section: High / Medium / Low with 1-sentence justification.
Escalate Low-confidence sections: propose 1–3 targeted follow-up questions.

4) Reference Pipeline (implementation sketch)

def gscp_pipeline(task, evidence_chunks, policies, model, n_self_consistency=3):
    log = {"task": task, "policy_ids": policies, "steps": []}

    preq = model.prompt(PRE_VALIDATE_PROMPT(task, policies))
    log["steps"].append({"pre_validation": preq})

    grounded = model.prompt(GROUNDED_GENERATE_PROMPT(task, evidence_chunks))
    log["steps"].append({"draft": grounded})

    # Optional self-consistency: sample n drafts and vote/merge
    drafts = [grounded] + [model.prompt(GROUNDED_GENERATE_PROMPT(task, evidence_chunks))
                           for _ in range(n_self_consistency - 1)]
    merged = majority_merge(drafts)  # deterministic merge/vote policy
    log["steps"].append({"self_consistency": {"n": len(drafts), "merged": merged}})

    checked = model.prompt(CONFLICT_CHECK_PROMPT(merged, evidence_chunks))
    log["steps"].append({"conflict_detection": checked})

    compliant = model.prompt(COMPLIANCE_PROMPT(checked, policies))
    log["steps"].append({"compliance": compliant})

    certainty = model.prompt(UNCERTAINTY_PROMPT(compliant))
    log["steps"].append({"uncertainty": certainty})

    artifact = build_audit_artifact(task, evidence_chunks, drafts, checked, compliant, certainty, policies)
    return compliant, artifact

Key traits

  • Deterministic merge policies reduce randomness.
  • Artifacts (prompts, model version, citations, policy results) are emitted to your Compliance Ledger.

5) The AI Compliance Ledger (minimal JSON spec)

Emit one record per final output:

{
  "id": "case-2025-08-19-001",
  "timestamp": "2025-08-19T19:12:00-07:00",
  "model": {"name": "llm-x", "version": "v4.5", "temperature": 0.2},
  "prompts": {
    "pre_validate": "...",
    "generate": "...",
    "conflict_check": "...",
    "compliance": "...",
    "uncertainty": "..."
  },
  "evidence": [{"sid": "S1", "hash": "sha256:..."}, {"sid": "S2", "hash": "sha256:..."}],
  "citations": [{"claim": "#1", "supports": ["S1","S3"]}],
  "checks": {
    "contradictions": 0,
    "unsupported_claims": 1,
    "pii_redactions": 2,
    "policy_pass": true
  },
  "uncertainty": [{"section":"Diagnosis","confidence":"Medium"}],
  "human_review": {"required": true, "reason": "low confidence in section 2"}
}

6) Evaluation: proving GSCP works

Track pre/post-GSCP metrics on a representative eval set:

  • Hallucination rate (unsupported claims / total claims)
  • Contradiction rate (internal + vs. evidence)
  • Citation coverage (% claims with evidence)
  • Compliance violations (policy checks failed)
  • Escalation precision (fraction of escalated items that truly required human review)
  • Latency & cost (per output; include self-consistency overhead)

Adopt a gated launch: require thresholds (e.g., hallucinations <1%, contradictions <0.5%) before moving from pilot → production.

7) Deployment Patterns

7.1 Prompt-Oriented Development (POD)

  • Version every scaffold and template.
  • Wrap prompts in Prompt APIs (callable functions) used by services and agents.
  • Add CI/CD tests: regression suites with gold-standard answers & policy checks.

7.2 Agentic Workflows

  • Planner agent runs Pre-Validation; Retriever binds evidence; Writer drafts; Verifier performs conflict/compliance checks; Governor decides escalate vs. release.
  • All tools emit to the same ledger schema.

7.3 Cost/Latency Controls

  • Enable adaptive depth: skip self-consistency for low-risk tasks; increase depth for high-stakes sections only.
  • Cache frequent retrievals and re-use verified snippets with content hashes.

8) Domain Patterns (quick recipes)

Healthcare (HIPAA/GDPR)

  • Force no external knowledge; require ICD/CPT code checks; automatic PHI redaction; uncertainty → clinician review.

Finance (SOX/SEC/ESMA):

  • Numeric reconciliation step; variance thresholds; template-locked narrative; evidence hashes from data warehouse.

Critical Infrastructure (NERC CIP):

  • Sensor-linked citations; severity rules; playbook-compliant recommendations; operator acknowledgment workflow.

9) Risk Register & Mitigations

  • Prompt Injection: sanitize inputs; never merge untrusted content into instruction channel; run verifier against a clean system prompt.
  • Retrieval Drift: pin sources via hashes + timestamps; alert on content change.
  • Over-redaction vs. utility: tune PII policies with precision/recall review; whitelist clinical or legal terms as needed.
  • Model updates: canary deploy; re-score on the same eval set; freeze prompts for critical reporting periods.

10) Quick-Start Checklists

Engineering

  • Define scaffold stages and prompt templates
  • Implement evidence hashing and citation enforcement
  • Add self-consistency (configurable n)
  • Emit ledger JSON on every run

Governance

  • Map policies → machine-checkable rules
  • Approve uncertainty thresholds & escalation paths
  • Stand up dashboards for contradictions, unsupported claims, PII events
  • Establish rollback & incident review procedure

Conclusion

GSCP turns LLMs into governed reasoning engines: grounded, self-checking, compliant, and auditable. With POD practices and a Compliance Ledger, you get measurable quality and regulator-ready traceability—without killing velocity. This is the practical path to enterprise-safe, domain-specific AI.

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