What is AI Critique Workflow?
An AI critique workflow uses a language model as a structured second reader to test a draft against explicit criteria such as clarity, logic, genre expectations, audience needs, consistency, or missing context.
What good ai critique workflow looks like
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.
- Define the evaluation criteria before sharing the draft.
- Ask the model to cite the specific passage that triggered each critique.
- Separate high-confidence structural issues from subjective preferences.
A practical structure to follow
Use these elements as a decision checklist, not as a rigid formula. The exact wording should still fit the reader, context, and purpose.
- Define the evaluation criteria before sharing the draft.
- Ask the model to cite the specific passage that triggered each critique.
- Separate high-confidence structural issues from subjective preferences.
How to write ai critique workflow step by step
- 1Choose three to five criteria that matter for the piece.
- 2Provide the draft and relevant context.
- 3Request ranked issues with passage-level evidence.
- 4Reject critiques that rely on invented context or unsupported assumptions.
- 5Test the strongest critique yourself before revising.
- 6Ask for a second pass only after changes are made.
8 AI Critique Workflow examples
Read the examples for structure and choices rather than copying surface wording. Notice what stays consistent and what changes with audience or purpose.
A fiction writer requests critique only on scene goals, conflict, and viewpoint consistency.
A graduate applicant asks whether each paragraph of a statement of purpose demonstrates preparation or merely asserts interest.
A product marketer asks whether landing-page claims are supported by the supplied proof points.
A manager asks whether a memo makes the decision and owner visible within the first two paragraphs.
A researcher asks the model to flag places where correlation is described as causation.
A blogger asks whether headings accurately describe the sections beneath them.
AI Critique Workflow templates
Replace every bracketed field with situation-specific information. A template is a starting structure, not finished copy.
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.
Common mistakes to avoid
- Requesting generic feedback without criteria.
- Treating every criticism as equally important.
- Accepting claims about facts, markets, law, or reader behavior without verification.
Final revision checklist
- Does the opening make the purpose clear quickly?
- Is every important claim, detail, or example doing a distinct job?
- Could a reader misunderstand any pronoun, transition, time reference, or instruction?
- Is the tone appropriate for the relationship and situation?
- Can you remove repetition without removing necessary context?
- If the writing contains factual claims, names, dates, quotations, or citations, have you verified them independently?
Questions about AI Critique Workflow
What is AI Critique Workflow?
An AI critique workflow uses a language model as a structured second reader to test a draft against explicit criteria such as clarity, logic, genre expectations, audience needs, consistency, or missing context.
What makes AI Critique Workflow effective?
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.
How do I write AI Critique Workflow?
Start with the purpose and reader, then work through the structure in order. Draft for meaning first, check the examples for pattern, and do a final revision for clarity, accuracy, tone, and unnecessary repetition.
What should I avoid when writing AI Critique Workflow?
Requesting generic feedback without criteria. Treating every criticism as equally important. Accepting claims about facts, markets, law, or reader behavior without verification.
What would make you change the recommendation for AI Critique Workflow?
Change the recommendation when the facts, evidence, reader, genre, authority, source text, story canon, risk level, or intended outcome changes enough that the current technique no longer serves the same writing job. A stronger editorial process states the signal that would change the advice, verifies the source of that signal, and then explains which constraint still has to remain true after the change.
When is guidance about AI Critique Workflow ready to publish?
Publish when the writing decision is useful and the supporting source is appropriate to the claim: the strongest available source tier has been checked, material disagreement or uncertainty is named rather than hidden, examples do not imply invented facts, and any recommendation is no stronger than the evidence, story canon, authority, usage evidence, or verified project facts allow. If a consequential claim still depends on an unverified source, generated citation, disputed record, stale requirement, or unresolved contradiction, qualify it, revise it, or hold publication until the evidence improves.
Does every statement about AI Critique Workflow need a recent source?
No. Freshness should match the claim type. Current policies, prices, roles, platform behavior, research findings, market conditions, and other changeable facts need current verification. Stable grammar, primary literary texts, manuscript canon, durable craft principles, and original illustrative examples may not need a recent citation at all. First classify the material as fact, interpretation, recommendation, convention, or original example; then use the strongest source and recency standard appropriate to that category, while preserving attribution and uncertainty where they matter.
When should I use a first-party or primary source instead of a secondary source for AI Critique Workflow?
Use the first-party or primary source when the exact fact, quotation, current requirement, project/manuscript detail, policy, metric, or source text controls the conclusion. Use a strong secondary source when the job is synthesis, explanation, field-level context, or orientation and the secondary source is appropriate to that job. If a reader could act on the claim, if sources disagree, or if wording depends on an exact passage, number, rule, or current status, escalate to the controlling source of truth and record the source, version/date, and locator before publication.