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 query-letter review works from an explicit rubric instead of asking for a generic rewrite. It separates diagnosis from revision, identifies what evidence or manuscript context the model cannot know, preserves the author’s voice, and requires manual verification of word count, genre, comparable titles, submission instructions, and other factual metadata.
Diagnostic review prompt: Here is my query letter and a verified manuscript fact sheet. Review hook clarity, protagonist/goal, conflict/stakes, metadata, bio relevance, and closing. Diagnose first. Do not invent comps, market facts, or agent preferences. For each issue, quote the exact line, explain the reader problem, and suggest a revision principle before offering wording.
Voice-preserving revision prompt: Revise only [section]. Preserve every manuscript fact and the current level of formality. Give 3 options with different sentence structures, then explain what each option emphasizes. Flag anything that would require information not present in my source text.
Fact-verification handoff: Extract every factual or externally verifiable claim in this query—word count, genre/category, comps, credentials, publication history, market statements, names, dates, submission requirements—into a checklist. Do not verify them yourself; mark what I must verify manually and where the source should come from.
Before/after audit: Compare my original query with the revised version. List any change to plot facts, character motivation, stakes, genre positioning, tone, credentials, or certainty. Mark each as preserved, changed, or requiring manual review.
Constraint decision sheet for AI Query Letter Review: normal case → [ ]; harder constraint → [ ]; what must stay true → [ ]; what can change → [ ]; evidence/reader/genre check → [ ]; specialist skill to study next → [ ].
Misconception correction audit for AI Query Letter Review: shortcut or assumption → [ ]; why it fails here → [ ]; corrective move → [ ]; principle that must remain true → [ ]; goal-based next skill → [ ].
Failure-mode and reader-intent audit for AI Query Letter Review: visible symptom → [ ]; likely underlying problem → [ ]; reader need → [ ]; competing technique considered → [ ]; correction → [ ]; evidence/genre/format constraint → [ ]; final verification → [ ].
Boundary-and-tool-choice audit for AI Query Letter Review: reader objection → [ ]; actual writing job → [ ]; evidence/genre/format boundary → [ ]; tempting alternative → [ ]; why it is the wrong tool here → [ ]; correct adjustment → [ ]; final verification → [ ].
Evidence/support and mastery ladder for AI Query Letter Review: minimum acceptable → [ ]; strong → [ ]; expert → [ ]; audience/genre constraint → [ ]; evidence or story support → [ ]; boundary that would require a different technique → [ ].
Certainty and expert-exception audit for AI Query Letter Review: statement or choice → [ ]; evidence/canon/authority supporting it → [ ]; confidence level → [ ]; uncertainty that must be named → [ ]; expert exception → [ ]; nearest alternative technique → [ ]; why the final choice fits better → [ ].
Purpose-and-verification decision audit for AI Query Letter Review: real writing job → [ ]; plausible choice A → [ ]; plausible choice B → [ ]; downstream consequence of each → [ ]; facts/evidence/canon to verify → [ ]; reader or genre boundary → [ ]; final purpose-fit reason → [ ].
Publication-readiness audit for AI Query Letter Review: 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 Query Letter Review: 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 Query Letter Review: 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 Query Letter Review: 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 → [ ].