Use this as the readiness checklist before a natural-language SQL system moves from demo to finance production.
An NL-to-SQL evaluation checklist is a readiness tool that confirms the system has scoped workflows, tiered golden examples, semantic-layer checks, answer-correctness evals, dataset-quality gates, role-sliced replay, drift monitoring, CI gates, and audit-ready evidence.
The checklist should fail a weak demo quickly.
A useful checklist is not polite. It should expose whether the system has the evidence needed for finance production.
Scope is named
Personas, tenants, workflows, model versions, datasets, and permissions are documented.
Golden set exists
Questions are tiered by materiality and include expected answers, role context, and data dependencies.
Semantic layer is tested
Metric definitions, synonyms, tenant overlays, and sample values are evaluated before SQL generation.
Answer correctness is scored
The full trace is scored against expected final answers, charts, caveats, and refusals.
Dataset quality is gated
Type, completeness, range, freshness, currency, and reconciliation checks run before answers are trusted.
RBAC is replayed
Material questions are tested under the roles and tenant slices that will use the product.
Drift is monitored
Known tier-1 questions are replayed over time and alerts fire when scores degrade.
CI gates block regressions
Releases fail when material questions regress below threshold.
Memorandum can be exported
The system can produce opinion, thresholds, exceptions, owners, remediation, and working papers.
Score readiness by evidence, not aspiration.
For each line, assign 0 if missing, 1 if manual or partial, and 2 if automated, versioned, and reviewable. Anything below 12 should stay out of broad finance production.
SCORE | MEANING | ACTION |
|---|---|---|
0-8 | Demo surface | Do not expand; build the working papers first. |
9-13 | Pilot surface | Limit scope; remediate missing evidence before scaling. |
14-18 | Production candidate | Run formal audit and close material exceptions. |
The checklist should point at the audit.
If a team cannot complete the checklist, the next step is not more prompt tuning. It is an AI Audit that creates the working-paper package.
The NL-to-SQL evaluation checklist for finance, answered plainly.
It can work as a lead magnet, but the public version should be useful on its own. The buyer should be able to see the operating bar before deciding whether they need help.
A high score means the evidence exists, not that the system is perfect. For production finance use, material questions still need threshold-based replay and exception review.
Parts of it can. The finance-specific requirements are materiality, audit vocabulary, board-grade questions, and stronger dataset-quality evidence.
Keep the evidence trail connected.
Golden dataset for NL-to-SQL
The checklist depends on a real golden set.
How to audit an NL-to-SQL system
The audit process behind the checklist.
AI Evals
TrustEvals service work for production eval layers, golden sets, and release gates.
If a finance AI answer can move an operating decision, the evidence behind it needs to be readable after the answer is gone.
Bring one workflow, vendor, or AI portfolio. We will map the evidence needed for finance leaders to fund, ship, or stop it.