# ADR 001: Generic grammar correction with GECToR-2024

- HTML version: https://robbiepalmer.me/projects/agent-first-writing/adrs/001-generic-grammar-correction-with-gector
- Project: Agent-first Writing Editor (https://robbiepalmer.me/projects/agent-first-writing.md)
- Status: Rejected
- Date: 2026-09-04
- Initiatives: Semi-autonomous Software Development (https://robbiepalmer.me/initiatives/semi-autonomous-software-development.md)

# Context

The editor must close the gap between correct sentences and publishable prose.
GECToR-2024 offered a lightweight way to test whether generic grammatical error
correction could close that gap without a larger editorial model.

I ran existing architecture decision records through GECToR-2024. It proposed
some useful corrections. They improved the input, but the result remained
repetitive, over-explained, and harder to absorb than the final published
writing.

Serving it also failed the project's hardware constraint. The model would not
fit on the GeForce GTX 1050 in the homelab. A remotely hosted GPU would add an
ongoing cost for a model that still leaves most of the editorial work to the
author.

# Decision

Reject serving GECToR-2024 as the editor's model layer. Its improvement does not
justify either new local GPU hardware or a paid remote GPU.

Keep the result as a baseline in the evaluation set. A later lightweight model
must target failure classes that deterministic rules miss. It must either fit
on the GTX 1050 or save enough editing work to justify remote inference.

# Consequences

The result still has value as a lower bound. It shows that grammatical error
correction improves some sentences without solving the larger editing problem.
Evaluation must also cover clarity, factual preservation, intent, terminology,
cognitive load, and voice.

This does not rule out every small model. It rules out adding one because it can
make locally plausible corrections. A model that needs remote GPU serving has a
higher bar because every use has a direct cost.

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