Decision-Grade Reasoning (DGR)

**Quick judgment**: Decision-Grade Reasoning (DGR) is a strong governance and traceability layer for LLM-driven decisions. It’s best suited for teams that need structured, audit-ready reasoning artifacts rather than just answers. The value is high for review-heavy or regulated environments, but it adds overhead and does not guarantee correctness, so it should be treated as a documentation and governance tool, not an oracle. **Key benefits** - Produces schema-valid JSON decision records with context, assumptions, risks, recommendation, and consistency check. - Enforces a consistent structure across tasks and models, improving reviewability and handoffs. - Modes (dgr_min / dgr_full / dgr_strict) let you trade off speed vs. depth based on stakes. - Governance guidance encourages clarifications, explicit uncertainty, and risk-based escalation. **Main risks / limitations** - Does **not** improve factual correctness by itself; it can produce well-structured but wrong or incomplete reasoning. - Risk of a false sense of safety if stakeholders equate structured output with reliability or regulatory compliance. - Additional latency and token overhead compared to direct answers, especially in `dgr_strict` mode. - Relies on the underlying model to honor the schema and governance rules; occasional schema or policy drift is still possible. **Recommended scenarios** - High-visibility or high-stakes decisions where you need an auditable reasoning trail (e.g., major product changes, security decisions). - Incident management and postmortems where structured capture of assumptions, risks, and rationale is required. - Compliance, internal audit, or quality-management processes that demand consistent decision documentation. - Standardizing decision records across multiple teams and models in larger organizations, with artifacts stored in tickets or logs.