KKissan Ki Pehchan
AI and Knowledge

Confidence and Research-Depth Policy

Multidimensional confidence, risk-adjusted verification and adaptive research budgets.

BlueprintVersion 0.25 Aug 2026

Dimensions

Dimension Question
Knowledge confidence Does the model understand the general subject?
Context completeness Are case-specific facts adequate?
Evidence support Are material claims supported?
Local applicability Does evidence fit crop, district, stage and practice?
Freshness Could rules, weather or research have changed?
Alternative risk Is another explanation close enough to matter?
Source agreement Do observations and sources agree?
Consequence severity What happens if the action is wrong?

Modes

Mode Use
MEMORY_ONLY Stable, low-risk explanation with enough context
ASK_FOLLOWUP One farmer fact would materially improve the answer
LOCAL_RETRIEVAL Departmental applicability or prior cases matter
CONTEXT_TOOLS Weather, soil, terrain or history matter
FOCUSED_WEB Recent, unusual, contested or missing evidence
DEEP_RESEARCH High consequence, rare case or source conflict
HUMAN_ESCALATION Evidence remains insufficient or authority is exceeded

Mandatory verification

Regardless of subjective confidence, verify current product registration; dose, dilution, interval and pre-harvest period; current weather/outbreak status; local restrictions; emerging threats; and high-consequence claims.

Research budget

Define maximum rounds, sources, domains/source classes, elapsed time, token/cost ceiling and escalation conditions.

Calibration

Calibrate model confidence on held-out local cases using reliability diagrams, expected calibration error and action accuracy by band. Thresholds vary by consequence.

Stop rule

Stop when the next action has low expected information gain relative to cost and evidence supports the proposed action at the required risk level. Otherwise continue or escalate.