How to ask VectorCourt
VectorCourt grades how well-grounded your question is. It will cap confidence on an under-specified question by design — and it will never invent facts about your situation to fill the gap. The fix is not "ask again"; it is to include the grounding the decision actually needs.
Why this matters: a thin question earns a low-confidence verdict not because the Council is weak, but because the answer depends on facts only you have. Evidence search cannot supply your operating environment, your fallback policy, or your validation data. Supply them and the cap lifts.
A worked example
Thin question · 39% confidence
"Should we replace our manual QA inspection line with machine vision? 200K units/month, 0.2% defect rate, $400K budget."
States volume, defect rate, and budget — but says nothing about operating environment, degraded mode, human override, or validation evidence. Confidence is capped and the verdict flags the missing grounding.
Grounded question · 57% confidence
Same decision, plus: factory floor / 3 shifts; 1.8s takt time; surface defects via 2D camera; conveyor + reject-gate already installed; falls back to manual inspection when vision is down; QA supervisor has stop authority; a 3-week pilot agreed with inspectors on 99.6% of 50,000 units.
Same engine, same models — the grounding cap is gone because the facts are present.
What a well-grounded question needs, by domain
These checklists are read live from the engine that grades your question, so they always match what is actually required.
Platform scaffold
- Existing stack — current technologies, languages, and platforms in use.
- Current scale — present load and the projected growth you must support.
- Measurement basis — how you sense/inspect/measure (sensors, inspection, telemetry, metrics).
- Validation evidence — pilot data, simulation, field tests, or historical outcomes.
Software architecture
- Existing stack — current technologies, languages, and platforms in use.
- Current scale — present load and the projected growth you must support.
- Validation evidence — pilot data, simulation, field tests, or historical outcomes.
Infrastructure scaling
- Current scale — present load and the projected growth you must support.
- Timing — cycle time, takt, latency, throughput, or the deadline the system must meet.
- Measurement basis — how you sense/inspect/measure (sensors, inspection, telemetry, metrics).
Build vs buy
- Budget — the cost ceiling or investment available for the decision.
- Team size — how many engineers (and what expertise) will build/operate it.
- Reversibility — how hard it is to undo, and the rollback window.
- Validation evidence — pilot data, simulation, field tests, or historical outcomes.
Product decisions
- Scope and stakeholders — who is affected and what is in/out of scope.
- Budget — the cost ceiling or investment available for the decision.
- Validation evidence — pilot data, simulation, field tests, or historical outcomes.
Release validation
- Reversibility — how hard it is to undo, and the rollback window.
- Degraded mode — the fallback / fail-safe when the system cannot continue normally.
- Validation evidence — pilot data, simulation, field tests, or historical outcomes.
Pre-deployment risk assessment
- Reversibility — how hard it is to undo, and the rollback window.
- Degraded mode — the fallback / fail-safe when the system cannot continue normally.
- Validation evidence — pilot data, simulation, field tests, or historical outcomes.
Robotics and automation stops for clarification
- Operating environment — where it runs (facility, field conditions, region, deployment context).
- Failure tolerance — acceptable defect/error rate, safety envelope, and what a failure costs.
- Timing — cycle time, takt, latency, throughput, or the deadline the system must meet.
- Measurement basis — how you sense/inspect/measure (sensors, inspection, telemetry, metrics).
- Process/equipment constraints — actuators, equipment, capacity, materials, or route limits.
- Degraded mode — the fallback / fail-safe when the system cannot continue normally.
- Human override — who can stop or override it, and under what condition.
- Validation evidence — pilot data, simulation, field tests, or historical outcomes.
Manufacturing design stops for clarification
- Operating environment — where it runs (facility, field conditions, region, deployment context).
- Failure tolerance — acceptable defect/error rate, safety envelope, and what a failure costs.
- Timing — cycle time, takt, latency, throughput, or the deadline the system must meet.
- Measurement basis — how you sense/inspect/measure (sensors, inspection, telemetry, metrics).
- Process/equipment constraints — actuators, equipment, capacity, materials, or route limits.
- Degraded mode — the fallback / fail-safe when the system cannot continue normally.
- Human override — who can stop or override it, and under what condition.
- Validation evidence — pilot data, simulation, field tests, or historical outcomes.
Logistics optimization stops for clarification
- Operating environment — where it runs (facility, field conditions, region, deployment context).
- Failure tolerance — acceptable defect/error rate, safety envelope, and what a failure costs.
- Timing — cycle time, takt, latency, throughput, or the deadline the system must meet.
- Measurement basis — how you sense/inspect/measure (sensors, inspection, telemetry, metrics).
- Process/equipment constraints — actuators, equipment, capacity, materials, or route limits.
- Degraded mode — the fallback / fail-safe when the system cannot continue normally.
- Human override — who can stop or override it, and under what condition.
- Validation evidence — pilot data, simulation, field tests, or historical outcomes.
Process engineering stops for clarification
- Operating environment — where it runs (facility, field conditions, region, deployment context).
- Failure tolerance — acceptable defect/error rate, safety envelope, and what a failure costs.
- Timing — cycle time, takt, latency, throughput, or the deadline the system must meet.
- Measurement basis — how you sense/inspect/measure (sensors, inspection, telemetry, metrics).
- Process/equipment constraints — actuators, equipment, capacity, materials, or route limits.
- Degraded mode — the fallback / fail-safe when the system cannot continue normally.
- Human override — who can stop or override it, and under what condition.
- Validation evidence — pilot data, simulation, field tests, or historical outcomes.
Risk assessment
- Failure tolerance — acceptable defect/error rate, safety envelope, and what a failure costs.
- Reversibility — how hard it is to undo, and the rollback window.
- Validation evidence — pilot data, simulation, field tests, or historical outcomes.
Technology selection
- Existing stack — current technologies, languages, and platforms in use.
- Current scale — present load and the projected growth you must support.
- Budget — the cost ceiling or investment available for the decision.
Vendor evaluation
- Budget — the cost ceiling or investment available for the decision.
- Failure tolerance — acceptable defect/error rate, safety envelope, and what a failure costs.
- Certification/regulatory boundary — standards, compliance, or legal constraints that apply.
- Validation evidence — pilot data, simulation, field tests, or historical outcomes.
Incident and postmortem analysis
- Failure tolerance — acceptable defect/error rate, safety envelope, and what a failure costs.
- Measurement basis — how you sense/inspect/measure (sensors, inspection, telemetry, metrics).
- Validation evidence — pilot data, simulation, field tests, or historical outcomes.
Policy and governance stops for clarification
- Scope and stakeholders — who is affected and what is in/out of scope.
- Failure tolerance — acceptable defect/error rate, safety envelope, and what a failure costs.
- Timing — cycle time, takt, latency, throughput, or the deadline the system must meet.
- Measurement basis — how you sense/inspect/measure (sensors, inspection, telemetry, metrics).
- Degraded mode — the fallback / fail-safe when the system cannot continue normally.
- Human override — who can stop or override it, and under what condition.
- Certification/regulatory boundary — standards, compliance, or legal constraints that apply.
- Validation evidence — pilot data, simulation, field tests, or historical outcomes.
Safety-critical system design stops for clarification
- Operating environment — where it runs (facility, field conditions, region, deployment context).
- Failure tolerance — acceptable defect/error rate, safety envelope, and what a failure costs.
- Timing — cycle time, takt, latency, throughput, or the deadline the system must meet.
- Measurement basis — how you sense/inspect/measure (sensors, inspection, telemetry, metrics).
- Process/equipment constraints — actuators, equipment, capacity, materials, or route limits.
- Degraded mode — the fallback / fail-safe when the system cannot continue normally.
- Human override — who can stop or override it, and under what condition.
- Validation evidence — pilot data, simulation, field tests, or historical outcomes.
- Certification/regulatory boundary — standards, compliance, or legal constraints that apply.
The short version
- State the decision and the options clearly.
- Include the grounding facts for your domain (above) — environment, constraints, fallback, who can override, and what evidence you already have.
- For high-stakes domains, a missing fact will pause for clarification rather than guess.
- A capped verdict is honest: it tells you exactly which facts to add, then re-ask.
- Asking about patents, prior art, freedom-to-operate, novelty, or infringement pulls in patent-corpus evidence automatically.