One hub. One method.
llmoptimisation.fr is a French-language methodological resource on optimising websites for AI answer engines. Independent, dated, sourced.
Six rules, non-negotiable.
Method over recipe
No list of hacks. A framework instead of a stack of tips. Every publication fits within the LOOP method, or explains why it diverges from it.
Dated, sourced, verifiable
Every page carries a publication date, a last-updated date, and, when relevant, verifiable external sources. Figures without a source are not published.
Anti-marketing stance
No "rank first on ChatGPT", no citation guarantees. The tone is precise and technical without empty jargon. We systematically push back against hacks.
Radical transparency
Any commercial collaboration, affiliation or sponsored content would be explicitly labelled as such, without exception. If a tool is cited without a disclosure, it serves the argument, not a sponsor.
French-first by default
Written in French for a knowledgeable French-speaking audience. An English version exists for reference publications, but French is the primary language of the hub.
Open and quotable
Most content is public, indexable, citable. No paywall, no sign-up required to read. Knowledge benefits from circulating freely, especially when the field is young.
Defining what we are, and what we are not.
What we are
- A French-language editorial resource on LLM Optimization.
- A methodological framework (LOOP) applied to optimisation for AI answer engines.
- An operational audit checklist, public and kept up to date.
- A reference point for building shared vocabulary in the field.
- An independent project with no affiliation or paid placement.
What we are not
- An agency or operational consultancy.
- A SaaS tool or citation-tracking platform.
- A tech news feed or AI current-events review.
- A tool or service directory.
- A promoter of hacks or visibility promises.
Four steps, per publication.
In-depth research.
Reading primary sources: research papers, official publisher documentation, reproducible tests.
Testing the method.
Every published method is tested on real cases before writing, never theorised in a vacuum.
Structured writing.
Plan set, self-contained passages, coherent schema.org, breadcrumb trail. The content follows the method it describes.
Review and update.
Cross-review before publishing. Each page is revisited at regular intervals; dateModified is only updated when substantive changes are made.
A comment, a correction, a contribution?
The project thrives on feedback. Factual error, missing source, use case to document, pillar proposal: all feedback is read.
Write to the editorial team →- Email hello@llmoptimisation.fr
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