ClickFused LLMO Framework: Make your organisation’s facts clear enough to retrieve, verify and evaluate in language-model answers. The ClickFused LLMO workflow centres on entity identity, source access, factual accuracy and reproducible answer checks.
For teams auditing whether model answers describe a business accurately. This differs from GEO’s broader discovery workflow by concentrating on factual representation and error diagnosis. Publishing a page does not directly retrain a public model.
Before you start
- An approved business fact register
- Canonical source pages for services, people and locations
- A fixed set of factual questions and an evaluation log
Step 1: Define the facts that must be correct
List the company identity, actual services, locations and contact details. Add source URL, reviewer and review date for each fact. Separate a service area from a physical office. Mark uncertain details unresolved instead of turning guesses into public claims.
Keep this output: A compact register of checkable facts.
Step 2: Publish one clear home for each fact
Use the About, contact, service and business-entity pages for their natural purposes. Link between them and fix contradictions. A short machine-readable information page can be a convenience, but it does not replace substantial public pages or create an inclusion guarantee.
Keep this output: A source map with stable, usable URLs.
Step 3: Separate search access from training
Review provider documentation with the website owner. OpenAI distinguishes OAI-SearchBot from GPTBot, so search visibility decisions and training preferences can be handled separately. A working robots rule is only part of access: hosting rules can still block requests. Do not promise that access leads to a mention.
Keep this output: A documented crawler decision and technical check.
Step 4: Build factual tests with expected evidence
Write questions that test identity, services, office location and limits. Record the expected fact and its source, not a preferred promotional sentence. Include a question the source cannot answer; a good evaluation allows an honest unknown.
Keep this output: A test set with expected evidence and unresolved cases.
Step 5: Diagnose errors before changing content
For every answer, save the question, model or product, date, search mode and cited sources. Classify the issue: stale source, conflicting pages, missing access, unsupported model claim or unclear question. Fix the demonstrated source problem, then repeat comparable tests.
Keep this output: An error log connected to a corrective action.
A worked example
Illustrative check: the source says a business has one Chennai office and serves Coimbatore remotely. An answer claiming a separate Coimbatore office contradicts the source. Correct conflicting website text if it exists; otherwise record the model error. Do not create an office page to match an incorrect answer.
Copy-and-paste prompt pack
Copy one prompt below into your preferred assistant. Replace the square-bracket inputs with approved facts or anonymised data. Review the answer against its sources before using it; these prompts prepare decisions and do not execute account changes.
Prompt 1: Build the fact register
From these approved pages [paste text and URLs], extract business name, services, people, physical offices, remote service areas and contact details. Return fact, source, date and unresolved conflict. Do not infer branches or credentials.Prompt 2: Create factual evaluation cases
Using this register [paste], create ten questions covering identity, service scope, location and limits. For each, give the expected supported fact, source and unacceptable unsupported assumption. Include two questions whose correct response is that the source is insufficient.Prompt 3: Evaluate an answer without guesswork
Question: [paste]. Exact answer and cited links: [paste]. Fact register: [paste]. Mark each material claim supported, contradicted or unresolved. Identify source defects separately from model errors. Recommend a correction only where evidence supports it.The improvement loop
Register facts → publish clear sources → check access → run the same factual tests → classify errors → correct the source where appropriate. Recheck after meaningful source updates, not simply to search for a flattering answer.
Practical tips and troubleshooting
- Track factual accuracy separately from citation frequency.
- Keep model/API evaluations separate from consumer assistant observations.
- A supplied brand prompt is useful for fact checks, but it cannot measure spontaneous brand discovery.
Validation checklist
- Every expected fact points to a real source.
- Physical offices and remote coverage are distinguished.
- Crawler settings follow provider-specific documentation.
- Unknowns and model variability remain visible in the log.
GitHub tooling and a reference screenshot
Promptfoo evaluation project ↗. Optional tooling for organising prompt tests and assertions. Review its provider configuration and data handling before use. A passed internal test does not certify how all public models will answer.
Sources and further reading
Platform guidance checked on 26 October 2026. Account interfaces and provider behaviour can change; inspect the current documentation before implementing a technical change.
Choose your next guide
Use the ClickFused SEO Framework for the shared planning foundation, then choose the workflow that matches your next task:
- ClickFused Meta Ads Framework
- ClickFused Google Ads Framework
- ClickFused AIO Framework
- ClickFused GEO Framework
- ClickFused AI SEO Framework
Need help applying the checklist to your own evidence? Contact ClickFused with the page or campaign scope you want reviewed.
