Question corpus
Brand, expertise, comparison, risk and decision questions tested consistently.
GUIDE 1401
LLMO & citations in AI answers
Observe what assistants claim, understand the sources they use and strengthen a verifiable public representation.
Operational briefLLMOreadability
CITEprovenance
EVALmeasurement
Operational brief
An AI answer is a synthesis, not a primary source.
Models may cite, paraphrase, combine or omit. Strategy is not about writing for an abstract machine, but making important facts available through coherent, identifiable and trustworthy sources. Measurement should track presence, accuracy, citations and stability over time.
AR / GUIDE 1401INDEPENDENT ANALYSIS
CITATION ARCHIVE
Provenance turns an answer into information.
A citation is useful only when its source, date, context and limits can be recovered. The observatory compares answers over time and documents gaps rather than drawing conclusions from one screenshot.
02 / Intervention areas
Intervention areas
Multi-model observation
Same wording, dates, assistants and search modes to identify variation.
Citation analysis
Displayed sources, probable sources, indirect reuse, freshness, authority and contradiction.
Entity accuracy
Name, role, locations, expertise, relationships, timeline and separation from namesakes.
Evidence strengthening
Official pages, verified profiles, reference publications, authors, structured data and external confirmation.
Correction loop
Detected error, probable origin, source correction, recrawl, new measurement and documented result.
03 / FIELD NOTES
Signals of unstable representation
An omission or error in an assistant often reveals a broader public-source weakness.
01OMISSION
The entity does not appear
Available sources do not establish relevance, authority or the relationship with the question.
02CONFUSION
Two entities are combined
Identifiers, biographies, profiles, domains and relationships do not support robust disambiguation.
03OBSOLESCENCE
Old information persists
The recent correction is less visible, connected or credible than the historical source.
04WEAK ATTRIBUTION
The answer is right but unattributed
The system synthesises information without preserving an identifiable brand or source.
04 / OUTPUTS
Generative-answer observatory
Useful measurement must be replayable and comparable; one screenshot does not describe a system.
Versioned corpus
Questions, language, intent, user profile, date and test conditions.
Answer journal
Model, mode, answer, citations, captures, errors and confidence level.
Representation score
Presence, accuracy, attribution, completeness, stability and risk.
Source map
Cited domains, dominant sources, relays, gaps and contradiction.
Correction backlog
Actions ranked by impact, control, propagation delay and verifiability.
Change report
Wave comparison, confirmed changes, uncertainty and next decisions.
05 / Method
Measure before influencing
The protocol limits premature conclusions: answers vary and depend on context, language and search mode.
- 01Fix the corpus
- 02Define conditions
- 03Test several systems
- 04Archive citations
- 05Qualify errors
- 06Trace source origins
- 07Correct publicly
- 08Replay measurement
06 / FAQ
Frequently asked questions
Clear answers on scope, limits, methods and engagement conditions.
Can an AI-assistant citation be guaranteed?
No. Clarity, authority and source availability can be improved, while a third-party model remains outside direct control.
Is one screenshot enough?
No. Answers vary by model, date, language, search mode, wording and sometimes user.
What does answer share measure?
How frequently an entity or source appears within a defined corpus under documented test conditions.
How can a hallucination be corrected?
Identify the claim, investigate its probable source, publish or strengthen the correction, then measure again after propagation.
Should content be produced specifically for LLMs?
Content should primarily be useful, explicit, sourced and technically accessible, with structure preserving meaning during extraction.
Are unlinked mentions useful?
They may support entity understanding, depending on context, source quality and disambiguation.
Which assistants should be monitored?
Those actually used by the relevant audiences, with several systems when the decision matters.
Is llms.txt sufficient?
No. It helps automated reading but cannot replace HTML, public sources, architecture or authority.
When should measurement be repeated?
After major correction, migration, model change or at regular intervals for priority subjects.
08 / CONTACT
Open a private channel
State the context and objective. You will receive a clear initial reading.