Skip to content

MISCELLANEOUS.md

Latest commit

 

History

History
100 lines (79 loc) · 5.61 KB

MISCELLANEOUS.md

File metadata and controls

100 lines (79 loc) · 5.61 KB

Miscellaneous: Cross‑Domain Usage & Safety Playbook

Version: 1.0
Status: Live
Source of Truth: https://github.com/FMI-Test/GenAI-RD/tree/main
Last Reviewed: January 25, 2026

This document explains how to apply the GenAI‑RD framework across domains (policy, engineering, finance, healthcare, etc.), with a pragmatic playbook, benefits vs. limitations, pitfalls and do/don’t, and model selection guidance tied to the “One‑Shot” methodology.

1. Purpose & Scope

  • Goal: Provide a methodical, repeatable path to use this repo’s constitutional framework in any domain.
  • Scope: Documentation‑first workflows; AI‑assisted analysis and auditing; human‑in‑the‑loop decision making; chain‑of‑custody verification.
  • Audience: Engineers, analysts, auditors, and collaborators mapping real problems to this framework.

2. Cross‑Domain Playbook (Methodical)

  1. Anchor to Constitution: Read and align with CONSTITUTION.md and GUARDRAILS.md.
  2. Clarify Roles: Map ownership using SHARED-RESPONSIBILITY.md.
  3. Define Intent: Write a short domain intent (What/Why/Boundaries) in the target folder.
  4. Run One‑Shot (Draft): Use a capable model to ingest the repo and draft domain‑specific artifacts (analysis, checklist, risk map).
  5. Run One‑Shot (Audit): Commission an independent auditor model to review findings for coherence, completeness, and risk.
  6. Human Verdict: Review auditor output; accept/reject changes; record decision and rationale in the domain folder.
  7. Attest & Trace: Capture evidence (links, hashes, commit IDs) per chain‑of‑custody guidance (see DEEP‑DiVE audit section).

3. Benefits vs. Limitations (Pro/Con)

  • Pro: Speed: Rapid onboarding and domain application via One‑Shot ingestion.
  • Pro: Traceability: Git/GitHub history + audit documents enable verifiable trails.
  • Pro: Reuse: Shared constitutional layer reduces duplication; projects inherit patterns.
  • Pro: Governance: Guardrails and shared responsibility clarify boundaries and accountability.
  • Con: Model Variance: Different LLMs exhibit different failure modes; require multi‑model review.
  • Con: Non‑Production Proof: One‑Shot is a POC method; reliability needs repeated runs (multi‑shot) and human oversight.
  • Con: Context Drift Risk: Long sessions may drift; keep artifacts short, explicit, and versioned.

4. Pitfalls & Do/Don’t

  • Do:
    • Anchor every decision to constitutional docs; cite evidence.
    • Record hashes and commit IDs for artifacts (attestation).
    • Use multi‑model peer review for critical domains.
    • Keep domain outputs concise; prefer checklists and tables.
  • Don’t:
    • Ship auditor output as truth without human review.
    • Mix domain artifacts with constitutional docs; keep inheritance clean.
    • Ignore model limitations; declare uncertainty and defer to sources.

5. Model Selection: “When Which Model?” (One‑Shot Triad)

  • Claude (Architect/Draft): Structured drafts, reasoning, and document generation; good for initial One‑Shot outputs.
  • Gemini (Auditor/Review): Cross‑checking, coherence, safety, and risk identification; good for independent audit runs.
  • OpenAI (Prosecutor/Stress): Adversarial tests and stress scenarios to reveal edge failures.
  • Human (Judge): Final authority; accepts/rejects; merges to main.
  • Pattern: Draft → Audit → Verdict; repeat if high stakes (multi‑shot).

Current Run Expectation: For stress testing and end‑to‑end review in this cycle, use the latest OpenAI model in the Prosecutor role, paired with Gemini for independent audit and human for final verdict.

6. Dependencies & Style Adherence

  • Runtime Dependencies: None required; compliance and audit rely on internal documents and Git/GitHub.
  • Evidence Dependencies:
  • Style:
    • Keep sections short, bulleted, and actionable.
    • Link to root docs instead of repeating text.
    • Prefer checklists for domain artifacts; add attestation lines (commit ID, SHA‑256).

7. One‑Shot: 5‑Line Audit Instruction

System: You are an AI Compliance Auditor.
1. Ingest the entire GenAI‑RD repository.
2. Operate per CONSTITUTION.md and GUARDRAILS.md.
3. Audit [TARGET_PROJECT_FOLDER] for risks and violations.
4. Produce [PROJECT]_AUDIT.md with evidence; do not modify files.

8. Conclusion & Next Steps

  • Conclusion: Cross‑domain application works when anchored to shared principles, explicit roles, and auditable evidence. One‑Shot accelerates analysis, but must be paired with independent audit and human verdict.
  • Next: Add domain‑specific checklists; run triad; attach evidence (links, hashes, commit IDs); record verdict.
  • References: README.md, CONSTITUTION.md, GUARDRAILS.md, SHARED-RESPONSIBILITY.md, DESIGN.md, COMPLIANCE.md.

Attestation (fill after commit)

  • Commit ID: [to be filled after commit]
  • File SHA‑256: [to be filled after commit]
  • Curator: [Human/Jurisdiction]
  • Date: [YYYY‑MM‑DD]