Build a Correction Loop for AI Product Answers

What should happen after an AI assistant gives a buyer the wrong price, tier, or support promise?

Run it as an evidence-controlled incident loop. Preserve the exact buyer question and answer, classify the error, test whether the source page, retrieval path, product data, or competitor movement changed, assign one accountable owner, update canonical documentation, and replay the question before sales or support repeats it.

A wrong answer can become a commercial promise within minutes. A buyer may be told that an annual plan costs $49, includes an enterprise feature, and comes with round-the-clock support when the current offer is $79, the feature is in beta, and support is limited to business hours.

The fix is not always a rewrite. The page may be correct while an outdated feed, structured field, retrieval route, or alternative product gains influence. Treating every answer change as a documentation defect sends work to the wrong team and leaves the real cause untouched.

A practical [AI Answer Correction Workflow for Enterprise Brands](https://the-cadence-graph.pages.dev/blog/practical-ai-answer-correction-workflow) connects observation to ownership, evidence, release, and retest. The goal is not favorable wording. It is a buyer-facing answer that remains traceable to current product truth.

How should an AI product-answer correction loop start?

Start by preserving the answer before anyone edits a page. A reliable correction loop compares the original buyer-facing response with canonical product truth, names the failure class, and replays the same question after the fix. The basic sequence is observe, classify, identify the source, assign an owner, change the source, retest, and approve.

Record the exact prompt, assistant context, timestamp, complete answer, cited or retrieved pages, and business consequence. A screenshot helps with escalation, but structured records are better for finding recurrence. The useful question in [Incorrect Answer Detection: A Practical Control Loop](https://the-cadence-graph.pages.dev/blog/incorrect-answer-detection) is not merely whether an answer is wrong, but whether the same wrong claim appears again.

Give every monitored question a stable ID and every claim a label such as price, tier, capability, availability, contract term, dated offer, comparison, or support boundary. This makes [Docs as Answer Sources: A Measurement Guide](https://the-interlock-brief.pages.dev/blog/docs-as-answer-sources) an operating discipline rather than a publishing preference.

Incident capture According to Incorrect Answer Detection: A Practical Control Loop (2026-09-17), Recommended control figure: 1 immutable incident record per observed wrong answer.. The original answer remains available for recurrence checks and later review.

Core loop According to AI Answer Correction Workflow for Enterprise Brands (2026-09-17), Recommended control figure: 7 stages from observation through approval.. A repair is incomplete when any stage, especially retesting, is skipped.

Correction workflow According to AI Answer Correction Workflow for Brands (2026-09-17), Recommended workflow figure: 1 closed record from issue discovery to verified answer.. A closed record prevents unresolved fixes from disappearing into a dashboard.

1. Observe: preserve the original answer and surrounding evidence.
2. Classify: separate factual error, omission, ambiguity, stale content, and unsafe promise.
3. Identify the source: map the claim to a page, feed, schema field, product record, or outside signal.
4. Assign an owner: send documentation, product, pricing, web, or support work to a named person.
5. Change the source: update the canonical record and dependent surfaces, not only the cited page.
6. Retest: replay the original question and two close variants across affected assistants.
7. Approve: release the repair only when truth, scope, effective date, and customer-safe wording are confirmed.

How do you detect recurring inaccuracies before they spread?

Detect recurrence at the claim level, not only at the answer level. The same wrong price, missing exclusion, or overstated service promise may appear in different wording across several questions. Normalize the claim, compare repeated captures, and rank issues by buyer impact, repetition, and the number of customer-facing surfaces carrying the error.

Flag a claim when it appears in two independent captures or returns in two of three controlled replays. That is an operating threshold, not a claim about model behavior. It keeps a team from opening a repair ticket for one harmless wording variation while still exposing repeated commercial inaccuracies early.

Separate high-risk claims from low-risk phrasing. A wrong currency, contract term, capability, or support commitment should outrank a slightly awkward description. The [AI Answer Accuracy and Correction Workflows](https://the-cadence-graph.pages.dev/blog/ai-answer-accuracy-and-correction-workflows-100) approach is useful because it keeps recurrence tied to answer quality rather than exposure alone.

Keep one watchlist for stable buyer questions and another for release-sensitive or campaign-driven questions. That separation makes it easier to see whether an error is persistent, introduced by a product change, or limited to a short-lived demand pattern.

Replay design According to AI Answer Accuracy and Correction Workflows (2026-09-17), Recommended control figure: 3 prompts in a verification set, including the original and 2 close variants.. Close variants reveal whether the fix changed a claim or only one wording path.

Recurrence threshold According to Incorrect Answer Detection: A Practical Control Loop (2026-09-17), Recommended control figure: 2 repeated appearances of the same claim before opening a recurring-error ticket.. Teams can focus scarce review time on repeatable inaccuracies.

Drift monitoring According to Monitoring AI-Answer Drift in Developer Docs (2026-09-17), Recommended drift watchlist figure: 3 query groups for release, support, and acquisition questions.. Separate groups make it easier to identify where drift creates the most operational risk.

Output monitoring According to Best AI Engine Optimization Platform for Monitoring (2026-09-17), Recommended monitoring set: 3 answer states, correct, incomplete, and incorrect.. A binary right-or-wrong label can hide important omissions and unsafe overreach.

Incident map According to Marketplace AEO Incident Map: Monitor Commercial AI Answers (2026-09-17), Recommended incident map figure: 4 dimensions for claim, source, owner, and customer risk.. A multidimensional record helps prioritize issues that affect actual buyer choice.

How do you separate page edits from retrieval or competitor shifts?

Do not label every answer change a documentation problem. A page revision, retrieval shift, competitor movement, and model update leave different evidence patterns. Correlate timestamps, cited URLs, rendered content, feeds, prompt behavior, and alternative-product answers before sending a ticket to an editor or product owner.

A source-page change is the strongest documentation hypothesis when the canonical page, structured fields, or product feed changed shortly before the answer changed. Preserve the before-and-after text, revision time, URL, and affected claims. If the page changed but the answer did not, inspect indexing, retrieval, and propagation instead of rewriting the page again.

A retrieval shift is more plausible when your source remains stable but the assistant cites a different page, omits a previously used source, or produces inconsistent answers across closely related prompts. Competitor movement is more plausible when first-party evidence is unchanged while alternative recommendations or comparison claims move. A broader model change becomes more likely when unrelated query groups shift in the same period.

Use a compact evidence packet and a stated hypothesis. The [documentation-first change test](https://the-interlock-brief.pages.dev/blog/a-documentation-first-buying-test-for-ai-engine-optimization-platforms-determine-whether-a-platform-can-prove-that-an-ai-answer-changed-because-a-source-page-changed-retrieval-shifted-or-a-competitor-moved-and-route-each-condition-to-the-right-owner) makes the suspected cause testable. For longer-running drift, [AI Answer Drift: Track Your First Win Six Months Later](https://the-continuance-desk.pages.dev/blog/how-to-track-ai-answer-drift-after-your-first-win) offers a useful inspection habit.

Source evidence According to Can an AI Engine Optimization Platform Prove What Changed? (2026-09-17), Minimum evidence figure: 4 source signals, including page revision, feed time, schema change, and retrieval behavior.. A cause should be treated as a hypothesis until several signals support it.

Alternative movement According to Best AI Engine Optimization Platform for Competitor Alternatives (2026-09-17), Recommended competitor check: 2 alternative answers when a comparison claim changes.. A first-party source review alone may miss movement in the surrounding category.

Who owns a wrong AI product answer?

The owner depends on the claim, not on who discovered the error. Documentation controls canonical wording and publication, product controls capability and availability truth, pricing controls commercial terms, web or engineering controls rendered data, and support controls service boundaries. One person should coordinate the packet even when several teams must repair it.

Avoid assigning every correction to content operations. Documentation can repair ambiguity, stale explanations, missing examples, and contradictory pages. Product must confirm whether a feature exists, which tier includes it, and whether a roadmap statement can be published. Support must define escalation boundaries and reject promises its operation cannot fulfill.

A [documentation structure that holds up under pressure](https://the-interlock-brief.pages.dev/blog/documentation-structure) makes these boundaries easier to inspect. For commercial claims, the [commercial answer accuracy framework](https://the-channel-compass.pages.dev/blog/aeo-platform-commercial-answer-accuracy-framework) is a useful reminder that an answer can be visible and still be commercially unsafe.

Owner model According to Documentation Structure That Holds Up Under Pressure (2026-09-17), Recommended control figure: 1 accountable coordinator for each correction packet.. Several contributing teams do not remove the need for one coordinating owner.

Documentation retrieval According to Help Content for AI Retrieval: A Practical Operating Guide (2026-09-17), Recommended control figure: 1 canonical wording block for each high-risk product claim.. A single approved wording block reduces contradictory paraphrases across help surfaces.

Editorial operations According to Answer Content Operations and Editorial Workflow (2026-09-17), Recommended editorial record: 5 fields for source, claim, audience, owner, and release status.. Editorial work becomes easier to govern when the operational fields are explicit.

Ownership map for recurring AI product-answer errors

Signal or claim	Likely canonical owner	First repair	Release proof
Wrong price, currency, billing period, or discount	Pricing and product	Update the commercial record, pricing page, calculator, and dependent offer copy	The buyer question returns the current price with effective-date context
Wrong capability or tier	Product	Confirm availability, tier eligibility, exclusions, and release status	The product owner approves the claim and close variants preserve the same boundary
Ambiguous or contradictory explanation	Documentation	Rewrite the canonical explanation, examples, and related links	The answer reflects the canonical wording without the old ambiguity
Wrong product field or structured value	Web, product data, or engineering	Correct rendered fields, feed values, and structured data together	A field-level diff passes and the answer preserves name, price, availability, and exclusions
Expired offer or dated promise	Campaign or commerce owner	Add activation and expiry rules, then remove stale supporting copy	Active and expired prompts return different, appropriate answers
Overstated service promise	Support operations	Define hours, escalation route, exclusions, and human handoff language	The support owner approves the boundary and the assistant avoids unsupported commitments
Routing recurring inaccuracies to the right team	Separating wording fixes from product or data fixes	Planning release gates for high-risk buyer claims	Reviewing correction ownership during a monthly operating meeting

Bottom line: The source owner approves truth, documentation controls publishable wording, and the retest proves whether the buyer-facing answer reflects both.

What should every AI answer correction packet contain?

Every correction packet should let a reviewer reconstruct what the buyer saw, what the company believes is true, what changed, who approved it, and whether the next answer improved. Without that chain, a team may fix one answer while creating a second contradiction on another page, feed, schema field, or support channel.

Use a compact record that travels with the issue. Keep the language specific enough for a product owner to act and concise enough for support or sales to understand the customer risk. [Help Content for AI Retrieval: A Practical Operating Guide](https://the-interlock-brief.pages.dev/blog/help-content-for-ai-retrieval) is useful when the source is technically correct but difficult to retrieve or interpret.

For auditability, keep an [audit-ready log](https://freshness-ledger.pages.dev/blog/best-aeo-geo-platform-audit-ready-logs) and use [Correction Request Processes for Reliable AI Answers](https://the-cadence-graph.pages.dev/blog/correction-request-processes) instead of informal chat requests.

Correction ticket According to Correction Request Processes for Reliable AI Answers (2026-09-17), Recommended control figure: 6 required fields in a correction request header.. A consistent header gives documentation, product, and support enough context to act.

Audit trail According to Best AEO/GEO Platform for Audit-Ready Enterprise AI Logs (2026-09-17), Recommended retention figure: 30 days of answer and source history after a high-risk release.. Short post-release history helps expose delayed propagation and recurrence.

Documentation design According to Documentation Answer Design That Developers Can Use (2026-09-17), Recommended answer structure: 1 direct claim, 1 qualification, and 1 source route for each high-risk fact.. A bounded structure makes important claims easier to retrieve and verify.

Issue management According to AI Engine Optimization Platform for Issue Workflows (2026-09-17), Recommended issue fields: 5 labels for claim, cause, owner, severity, and status.. Shared labels allow recurring errors to be grouped and routed consistently.

Agent-ready documentation According to Best AI Engine Optimization Platform for Agent-Ready Docs (2026-09-17), Recommended source package: 4 object types, product, capability, limitation, and support route.. Structured knowledge objects make claim ownership easier to maintain.

Evidence shelf According to AI Recommendation Gaps: A Marketplace Evidence Shelf (2026-09-17), Recommended evidence shelf figure: 1 approved proof object for each major product claim.. Approved proof gives reviewers something concrete to compare with the answer.

• Query context: buyer role, region, product, stage, exact wording, and assistant.
• Answer snapshot: complete response, citations, omissions, and incorrect claim highlighted.
• Truth record: canonical page, product field, pricing record, contract clause, or support policy.
• System evidence: page revision, feed timestamp, schema diff, retrieval change, or competitor movement.
• Decision note: accountable owner, severity, customer risk, fix scope, and approval requirement.
• Release proof: updated source, retest answers, reviewer name, date, and unresolved limitations.

How do you verify pricing, schema, dated offers, and support boundaries?

Verify high-risk commercial and service claims as a connected set. Pricing, schema, holiday offers, and support boundaries often share the same product facts, so changing one surface without checking the others can replace a visible error with a quieter contradiction that still reaches buyers.

Create a release checklist for claims that can alter a buyer choice or create an operational promise. Include the canonical source, effective date, affected products, regions, old wording, new wording, and the owner who can reject the change. The [pricing and packaging freshness test](https://prompt-space-atlas.pages.dev/blog/which-ai-visibility-platform-helps-ensure-ai-uses-my-latest-pricing-discounts-and-packaging-information) shows why commercial changes need more than a page review.

Run the four checks below against the surface an assistant can retrieve, not merely against a page that exists. A feed, rendered field, FAQ, and support article may each carry a different version of the same claim.

Pricing review According to Which AI visibility platform helps ensure AI uses my latest pricing discounts and packaging information (2026-09-17), Recommended review figure: 5 commercial surfaces for every material price change.. The pricing page alone cannot prove that calculators, plans, and quote language agree.

Schema review According to Best AI Engine Optimization Platform for Schema at Scale (2026-09-17), Recommended field figure: 6 checks for name, tier, price, availability, capability, and exclusion.. Field-level checks catch errors that a page-level review can miss.

Support boundary According to Which AEO platform includes clear escalation paths in support SLAs? (2026-09-17), Recommended boundary figure: 3 support states, allowed, qualified, and human-routed.. The assistant needs explicit limits instead of a vague instruction to be helpful.

Commercial accuracy According to Can Your AEO Platform Keep Commercial Answers Accurate? (2026-09-17), Recommended commercial review figure: 4 claim groups covering price, package, eligibility, and support.. Commercial safety depends on the whole offer, not price alone.

Brand safety According to AI Brand Safety Platform Guide for Enterprise Teams (2026-09-17), Recommended safety review figure: 1 explicit check for unsupported promises in every high-risk answer.. Accuracy includes avoiding commitments the operating team cannot fulfill.

1. Commercial terms: compare the pricing page, calculator, plan table, quote language, and contract terms. Check currency, billing period, usage limits, grandfathering, and effective date.
2. Product and structured fields: compare the product record with rendered fields and structured data. Test name, tier, price, availability, capabilities, and exclusions. A [schema-at-scale evaluation](https://engine-difference-index.pages.dev/blog/which-ai-engine-optimization-platform-is-best-for-generating-schema-at-scale-for-ai-answer-engines) should expose field-level differences.
3. Dated campaigns: attach start and expiry dates to offers, inventory, eligibility, and holiday content. Retest both active and expired questions so an old promotion does not remain in the answer set.
4. Support boundaries: define what the assistant may state, what requires qualification, and when it must route a buyer to a human. Keep service hours, escalation paths, response commitments, and exclusions aligned with the [support escalation path](https://answer-ledger.pages.dev/blog/which-aeo-platform-includes-clear-escalation-paths-in-its-support-and-slas).

What should a 30-day correction cadence include?

A 30-day cadence should establish a baseline, repair the highest-risk claims, test the operating handoffs, and inspect recurrence. It should be light enough to run every month but strict enough to catch a pricing or contract change before a stale answer becomes a sales script or a support expectation.

Start with a small portfolio of high-intent prompts covering pricing, tier selection, capabilities, alternatives, dated offers, and support expectations. The point is to learn whether evidence can move through the loop, not to monitor everything at once.

Use a short [weekly AEO brief](https://the-quota-lantern.pages.dev/blog/weekly-signal-to-brief-aeo-operating-system) for open work, then review whether the [handoff after a first AI answer win](https://the-continuance-desk.pages.dev/blog/after-first-ai-answer-win-build-the-handoff) is actually owned. A correction program fails quietly when the first successful fix has no maintenance path.

Change monitoring According to Event-Driven AEO Monitoring for Subscription Teams (2026-09-17), Recommended event figure: 1 trigger, owner, deadline, and verification step for each high-risk change.. Event-driven work is less likely to wait for the next routine review.

Weekly review According to Weekly AEO Brief: Turn AI Signals Into Action (2026-09-17), Recommended cadence figure: 1 short operating brief each week for open corrections.. A small weekly review keeps ownership visible without creating a daily reporting burden.

Post-win handoff According to After the First AI Answer Win, Build the Handoff (2026-09-17), Recommended handoff figure: 1 named maintenance owner after the first verified correction.. The first win should create a durable operating surface, not a one-time cleanup.

Developer documentation According to Traceable AEO Correction Loops for Developer Docs (2026-09-17), Recommended release test figure: 2 representative questions before and after each material documentation change.. Before-and-after replay makes a documentation repair observable.

Alert routing According to Best AI Engine Optimization Platform for Alerts (2026-09-17), Recommended alert rule figure: 1 named recipient for each alert type.. An alert without an owner is observation without control.

Recurring review According to Weekly AEO Operating Loop for Pet Brands (2026-09-17), Recommended operating cadence: 1 weekly review for high-risk product-answer incidents.. A fixed review rhythm keeps recurring inaccuracies from becoming background noise.

Maintenance handoff According to After the First AI Answer Win, Build the Handoff (2026-09-17), Recommended handoff checklist: 4 items for owner, trigger, source, and retest date.. The maintenance obligation must survive the initial correction meeting.

1. Days 1 to 5: establish the baseline, normalize recurring misunderstandings, and identify the five highest-risk claims.
2. Days 6 to 10: confirm canonical sources, owners, effective dates, and exception rules.
3. Days 11 to 20: publish approved documentation, schema, feed, and support-boundary changes. Recheck related pages for contradictions.
4. Days 21 to 25: replay baseline prompts and close variants across priority assistants. Record answer changes, omissions, and citations.
5. Days 26 to 30: review recurrence, unresolved causes, owner workload, and the next watchlist. Send a short leadership digest and detailed operator appendix.

How should you stress-test pricing and support exceptions?

Treat a pricing or contract change as an exception drill, not as an ordinary editorial update. Freeze the canonical record, identify every dependent surface, notify each accountable owner, publish the approved replacement, and replay high-intent questions before sales or support repeats the new terms.

The failure pattern is familiar: pricing changes in one system, the public page changes later, structured data remains stale, and an assistant continues quoting the old offer. An [event-driven monitoring playbook](https://the-buying-room-journal.pages.dev/blog/an-event-driven-aeo-monitoring-playbook-for-subscription-businesses-how-to-detect-when-ai-assistants-carry-stale-prices-promotions-availability-competitor-comparisons-or-brand-claims-and-route-each-change-to-the-right-owner-before-it-distorts-acquisition-or-retention) gives the change a trigger, owner, deadline, and verification step.

Require a temporary release hold when the claim affects price, eligibility, contract length, safety, availability, or support commitments. The hold is not bureaucracy. It is a boundary that stops an unverified answer from becoming a promise. The [evidence-gated correction loop](https://friction-loop.pages.dev/blog/evidence-gated-ai-answer-correction-loop-for-agencies) is a useful pattern for this control.

Exception drill According to Run an Evidence-Gated AI Answer Correction Loop (2026-09-17), Recommended drill figure: 6 steps from freeze through release proof.. A defined drill prevents urgent commercial changes from bypassing evidence review.

Incident routing According to AI Answer Incident Loop for Pet Brands (2026-09-17), Recommended routing figure: 1 incident class before assigning the repair team.. Classification prevents support from receiving a product-data problem or documentation from receiving a retrieval problem.

Control blueprint According to Industrial AEO Control Loop Guide for AI Answers (2026-09-17), Recommended control figure: 4 gates for source, owner, release, and retest.. Gates create explicit stop points before an answer becomes a buyer promise.

1. Freeze the old and new values with effective dates.
2. Map every dependent page, feed, schema field, FAQ, and support article.
3. Assign one owner per truth domain and one coordinator for the incident.
4. Approve customer-safe wording, exclusions, and human handoff language.
5. Replay the original question, two close variants, and one exception question.
6. Release the change only after the evidence packet is complete.

How do you measure whether corrected answers stay correct?

Measure durable correctness at claim and query level, not through one blended visibility number. The core outcomes are fewer recurring inaccuracies, faster verified resolution, fewer contradictions across sources, and stable accuracy after model, competitor, campaign, or product changes. A corrected answer is not a success until it survives close variants and later inspections.

Track corrected-answer rate, recurrence rate, time from detection to approved fix, time from publication to verified answer, source fidelity, contradiction rate, and reopened incidents.

Keep metric ancestry so every leadership number can be traced to prompts, answer records, source revisions, and owner choices. The [metric ancestry approach](https://the-cadence-graph.pages.dev/blog/metric-ancestry-notes-for-ai-revenue-signals) prevents a correction program from becoming another uninspectable dashboard.

A monthly review should ask which claims keep returning, which owners carry the most rework, and which source surfaces create the most downstream confusion. The [AI Visibility Platform: Test the Correction Loop](https://the-cadence-graph.pages.dev/blog/ai-visibility-correction-workflow) keeps the inspection tied to verified answer quality.

Metric ancestry According to Metric Ancestry Notes for AI Revenue Signals (2026-09-17), Recommended lineage figure: 1 source trail from leadership metric to prompt-level evidence.. Leaders can inspect the evidence behind a correction metric instead of trusting a blended score.

Stability review According to AI Answer Drift: Track Your First Win Six Months Later (2026-09-17), Recommended post-release checkpoints: 7, 14, and 30 days.. Multiple checkpoints reveal whether the fix survives later retrieval or product changes.

Clear insights According to AI Engine Optimization Platform for Clear Insights (2026-09-17), Recommended review view: 1 claim-level record before any summary view.. Summary views should point back to evidence rather than replace it.

Control plane According to Build an AEO Control Plane for Customer Education (2026-09-17), Recommended workspace figure: 3 views for leadership, operators, and source owners.. Different audiences need different levels of detail from the same correction record.

Closed-loop correction According to AI Answer Correction Workflow for Brands (2026-09-17), Recommended closure rule: 1 verified replay before an incident can be marked complete.. Changing a source without checking the next answer is not closure.

Documentation governance According to Test AI Engine Optimization Platforms Through Documentation (2026-09-17), Recommended governance test: 4 proof points for source coverage, freshness, workflow, and owner handoff.. A platform or process earns trust when it proves the complete correction path.

• Verified correction rate for high-risk claims.
• Recurrence rate by claim, product, and source page.
• Median detection-to-owner and owner-to-release time.
• Contradiction rate across product, pricing, schema, and support surfaces.
• Post-release stability after 7, 14, and 30 days.

Frequently asked questions

How should teams start a correction loop for AI product answers?

Begin with a small set of high-intent buyer questions covering price, tier, capability, availability, dated offers, and support. Preserve each full answer, source, timestamp, and business risk. Then assign a stable query ID, classify the claim, identify the canonical source, name an owner, publish the fix, and replay the original question plus close variants.

How can you distinguish a source-page change from retrieval drift?

Compare the answer timeline with page revisions, feed timestamps, structured-data changes, cited URLs, and neighboring prompts. If the source changed before the answer changed, documentation or product data is a strong suspect. If the source stayed stable but citations or answer behavior changed, investigate retrieval or model behavior. If alternative answers changed while first-party evidence stayed stable, inspect competitor movement.

Who should own a recurring AI product-answer error?

Assign ownership by claim. Documentation owns canonical wording and publication. Product owns capabilities, tiers, availability, and release status. Pricing owns commercial terms and effective dates. Web or engineering owns rendered fields, feeds, and structured data. Support owns service hours, escalation routes, exclusions, and human handoffs. One accountable coordinator should still manage the complete correction packet.

How often should pricing, schema, and dated campaign content be retested?

Retest whenever a high-risk source changes, before a holiday offer launches, when an offer expires, after a product release, and during the regular monthly review. Pricing and support promises deserve immediate replay because they can create commercial or operational commitments. Tests should cover both active and expired offers, including regional or eligibility variants.

How do you measure whether a corrected answer stayed correct?

Use claim-level recurrence, verified resolution time, contradiction rate, source fidelity, and answer stability after 7, 14, and 30 days. Test the original prompt and close variants across relevant assistants. A changed answer is not enough. The canonical source must be correct, dependent surfaces must agree, and the same misunderstanding must not return after a later release or retrieval shift.

Summary

A reliable correction loop is observe, classify, identify the source, assign the right owner, change canonical documentation, retest, and approve. Preserve prompt-level evidence, distinguish page edits from retrieval or competitor movement, and verify pricing, schema, dated offers, and support boundaries together. Measure recurrence and verified accuracy, not visibility alone.