| name | grounded-verifiable-gates |
| description | Use when an LLM or agent emits decisions or claims and you need to turn fuzzy output into a verifiable, regression-protected signal. |
Make AI Output Verifiable: Grounding + Deterministic Gates + Eval Harness
Never let a model's self-assessment be the decision. Force every claim to cite source text that literally matches, compute the outcome with a pure tested function over only the grounded claims, and protect that function with an eval harness that runs the real production path and fails CI on regression.
When to use
Reach for this whenever a model's output influences what happens next: a judge that approves or flags, an extractor that fills a record, a classifier that routes, an agent that decides to act. The higher the cost of a wrong, confidently-stated answer, the more this applies.
Red-flag thoughts that mean STOP and apply this skill:
- "The model rated it 0.9, so we'll pass it." — a self-rating is not a verdict.
- "It cited a source, so it must be grounded." — citing is not matching; check the quote against the text.
- "We'll eval against a simplified harness / a copy of the prompt." — then you're measuring a thing you don't ship.
- "The thresholds feel about right." — feelings are not calibration.
- "Precision looks great" (where the same model graded itself) — that number is circular.
The rule
- Demand a verifiable citation per claim. Require each finding to carry a quote, and verify that quote is a literal substring of its cited source after normalizing both through one shared text pipeline (same Unicode form, whitespace, casing rules). A quote that does not match is marked
ungrounded.
- Drop ungrounded claims before they can matter. The decision function reads only grounded findings. By construction, a hallucinated quote cannot move the outcome — there is no path from an unmatched claim to a verdict.
- Make the verdict a pure function. Compute the outcome deterministically from the grounded findings against pinned default thresholds. Unit-test it in isolation. The model proposes findings; code decides.
- Drive production and review from the same gate. The routing edge in production and any human-review UI must call the identical gate. Reviewers see exactly the signal that routed the run — no parallel logic, no drift.
- Build the eval harness on the real path. The harness must invoke the unchanged production use-case and the same decide gate, and feed fixtures through the same text pipeline. It measures the judge you ship, not a re-implementation of it.
- Turn metrics into a CI pass/fail. Pin gates in version control as tunable config and exit non-zero on breach. Favor judge-independent gates: recall (did it catch real issues), grounding rate (are claims verifiable), over-flag rate (does it cry wolf on good inputs). Example defaults:
min_recall 0.70, max_over_flag 1.0, min_grounding 0.80.
- Calibrate, then prove discrimination. Set thresholds from a real run with documented provenance (which model, which inputs, when). Prove the gates actually separate quality: a strong model clears them; a deliberately weaker one fails. A gate everything passes is decoration.
- Report honestly. Headline the judge-independent metrics. Report judge-dependent ones (e.g. precision, when the model grades its own work) as bounds, and disclose the self-judging bias explicitly.
- Verify an agent's "done" by mechanism, not by its word. When an agent reports it performed an action, confirm it from the world — the file's new content, the record's existence, a zero exit code — and fail closed when the check cannot confirm. A worker that says "done" having written nothing must read as nothing-happened, not as success; its self-report is a proposal, and the mechanical check is the verdict.
Why
An LLM's stated confidence and its actual correctness are only loosely correlated, and a fluent wrong answer is more dangerous than an obviously broken one. If the model's own number is the gate, a hallucination flows straight to the outcome and you have no tripwire. Grounding gives you a mechanical filter — unverifiable claims are inert. A pure verdict function gives you something you can unit-test and reason about. An eval harness on the real path gives you regression protection that actually reflects production; one on a re-implementation gives you false comfort that breaks silently when the prompt or pipeline drifts. And calibrated, version-controlled gates convert "it seemed fine in the demo" into a signal that blocks a merge. Skip these and quality becomes invisible until a user hits the failure — at which point you're debugging vibes, not a contract.
In practice
On the project this was distilled from — a hexagonal, human-in-the-loop AI agent (an LLM judge reviewing documentation, shipped to a cloud runtime) — docs/ARCHITECTURE.md states: "A finding whose quote is not literally present — a hallucination — is marked ungrounded and is ignored by the decision gate …" (the source continues "… and excluded from the proposal prompt"). The eval harness was built to reuse the production evaluation entrypoint and the domain decide gate unchanged, so, per docs/EVALUATION.md, "it measures exactly the judge the agent runs in production" — fixtures normalized through the same text pipeline as live input. The CI gates lived in evals/golden/labels.yaml beside the golden labels as config: min_recall_deterministic: 0.70, max_over_flag_good: 1.0, min_grounding_rate: 0.80, and the run exited non-zero on breach. Crucially, the team reframed precision honestly: because the model graded its own output, precision was reported as a bound with the bias disclosed, while recall and grounding — independent of the judge's self-opinion — carried the headline. You can apply all of this without knowing that project: substring-check quotes, gate on grounded findings only, eval the real code path, pin thresholds in CI, calibrate against a labeled run, and be honest about which numbers the model could game.
Anti-patterns
- Letting the LLM's own verdict or score be the decision, instead of a deterministic function of grounded findings.
- Scoring a parallel re-implementation (a copied prompt, a simplified scorer) rather than the actual production path, so eval numbers don't reflect what ships.
- Setting gate thresholds by intuition instead of calibrating against measured behavior — and never checking that a weaker model would fail them.
- Presenting a self-judged precision as the headline metric while hiding that the model graded its own work.
Enforcement
This skill's harness is its own enforcement: a CI job that runs the deterministic gate over the fixed corpus on every change and fails on regression. What a machine additionally holds: grounding invariants as assertions (every cited id exists, every span matches its source), thresholds living in config so a calibration change is a reviewable diff, and fail-closed semantics — an empty corpus or a skipped eval job is a FAIL, never a quiet green.
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