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AI Drafted the Docs. Your Job Is Decisions, Not Prose.

AI Drafted the Docs. Your Job Is Decisions, Not Prose. When a language model drafts documentation, the bottleneck shifts from writing to reviewing, and most review habits were built for scarce text. Teams respond by reading generated prose line by line, which spends attention on wording the model can regenerate in seconds. The better split separates labor by kind rather than by time: the model…

This article introduces a review pipeline for AI-generated documentation, shifting the bottleneck from writing to reviewing. The pipeline separates labor by kind rather than time, with the model producing candidates and a script extracting decision points for human review. It highlights why traditional line-by-line review fails and presents a four-step workflow with concrete artifacts produced at each step.

The workflow starts by defining ownership tiers in a YAML file, which declares what each documentation section may contain. Drafting occurs using a bounded prompt that specifies which sections to draft and which to leave empty. The script generates a review brief, a short list of decisions that require human judgment. This approach addresses volume acceptance and review theater patterns, ensuring that only valid content is merged into production.

Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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"My Agent Refused 96 Times": Building Self-Editing Agents with Hard Failure Modes

Originally published on tamiz.pro . In the early days of shipping LLM-based agents, we optimized for output volume. If the model could not find the answer, it often generated a plausible one anyway.

  • Agent refused 96 valid questions in testing
  • Demonstrated hard failure mode for insufficient context
  • Shifted focus to deterministic verification

The Gate Said No. Now What? A Triage Procedure for Rejected Agent Patches

A gate that rejects a patch is only half a policy. The other half is what happens after the rejection. In most pipelines, a failing agent patch produces one of three outcomes: a human stares at the…

  • Gate rejects patch, leading to three outcomes: human review, blind rebuild, or test deletion
  • Proposed triagegate.py script re-runs failing test, compares fixture hashes, freezes flakes
  • Class A: deterministic regression, Class B: fixture drift, Class C: flake with quarantine ledger

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