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