AI Critique Workflow Template 1
Evaluate this draft using only these criteria: [criteria]. For each issue, quote the passage and explain why it matters.
Start with structure, then replace every placeholder with information that is true, relevant, and natural for your context. These templates are intentionally skeletal so your final writing does not sound mass-produced.
A useful AI critique separates observations from proposed fixes, asks for evidence from the supplied draft, avoids pretending to know reader reactions with certainty, and leaves final editorial judgment with the writer.
Evaluate this draft using only these criteria: [criteria]. For each issue, quote the passage and explain why it matters.
Rank the issues by likely impact on [reader goal]. Separate objective inconsistencies from stylistic preferences.
Do not rewrite. Identify places where the draft assumes information the reader has not been given.
After my revision, compare version B with version A and tell me which original issues remain.
Publication-readiness audit for AI Critique Workflow: claim/advice/example → [ ]; strongest available source tier → [ ]; important disagreement or uncertainty → [ ]; what would change the conclusion → [ ]; publish now / qualify / revise / hold → [ ]; final verifier or source of truth → [ ].
Provenance and classification audit for AI Critique Workflow: draft statement/example → [ ]; classify as fact / interpretation / recommendation / convention / original example → [ ]; source or internal provenance → [ ]; source date / version / checked date → [ ]; attribution needed → [ ]; recency risk → [ ]; final publishable wording → [ ].
Source-record and update audit for AI Critique Workflow: statement / example → [ ]; external fact, project fact, interpretation, recommendation, convention, or original example → [ ]; controlling first-party / primary source → [ ]; secondary source if used → [ ]; source record / locator → [ ]; date or version checked → [ ]; update trigger → [ ]; publish / qualify / revise / hold → [ ].
Source-ledger and revision-history audit for AI Critique Workflow: claim / recommendation / example → [ ]; source or project record ID → [ ]; source status active / superseded / corrected / retracted / retired → [ ]; claim-level last verified date/version → [ ]; event that triggered recheck → [ ]; correction / withdrawal action → [ ]; reader-facing disclosure needed? → [ ]; what changed and why → [ ]; prior wording/version retained at → [ ].
Claim/source status audit: Item [claim/source/example] | Status [active / review due / stale / corrected / retracted / superseded] | Controlling source/version [x] | Last verified [date/version] | Stale trigger [x] | Correction severity [0–3] | Reader disclosure [none / note / correction / withdrawal] | Changelog entry [what changed + why].
Dependency and propagation audit: Claim / example / recommendation [x] | Controlling source / project record [x] | Dependency strength [direct / shared / advisory / independent] | Review trigger / reason code [x] | Needs-review scope [local / direct dependents / cluster] | Replacement source requirements [x] | Carry-forward decision [x] | Propagation targets [x] | Reader disclosure / changelog action [x].
Persistent editorial registry audit: Topic slug → [ ]; claim UID/key → [ ]; source UID/key → [ ]; dependency UID + strength → [ ]; source status/version → [ ]; open review UID/reason/priority → [ ]; replacement source if any → [ ]; reviewer + resolution → [ ].