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Human Overrides

Verist assumes humans can overrule AI output. Those decisions are not a side effect – they are state.

Why this exists

You upgrade a model. Three past decisions change. A reviewer fixes one decision manually. You recompute again a week later – the human correction still wins.

text
model v1 → computed = 0.42
human fix → overlay  = 0.90
model v2 → computed = 0.38
effective = 0.90  ← human override preserved

The three-layer state model

LayerSource
computedWhat steps produce
overlayHuman corrections
effective{ ...computed, ...overlay }
ts
import { effectiveState } from "@verist/storage";

const effective = effectiveState(snapshot);
// equivalent to { ...snapshot.computed, ...snapshot.overlay }

Overlay always wins. Human corrections survive recompute.

text
step → computed
review UI → overlay
recompute → computed updated, overlay unchanged

WARNING

Steps must never write to the overlay.

Why this matters

Without an overlay layer, you choose between:

  • Keeping human edits, but losing recompute
  • Recomputing, but losing human edits

Verist avoids that tradeoff.

How to apply an override

Overrides are an external concern. Your UI or review tool writes them to the overlay layer in storage.

Typical flow:

  1. Run a step and store the computed output
  2. Show the diff to a reviewer
  3. If they override, write to overlay

Design tips

  • Use small, targeted overrides (single fields) instead of large patches
  • Record a reason alongside the override
  • Treat overrides as audit events

LLM context: llms.txt · llms-full.txt
Released under the Apache 2.0 License.