G.E.O. LinkedIn

The question: if a user asked the question your post addresses to ChatGPT, would your content be a good direct answer?

The action: write with extraction in mind. Four operational rules:

1. Title phrased as a user query ("How to shorten your B2B sales cycle in 30 days" rather than "My thoughts on sales").
2. Direct answer in the first sentence — no narrative intro, no personal anecdote upfront. Synthetic answer first, nuances after.
3. Short self-contained paragraph structure — each paragraph must be extractable in isolation and keep its full meaning. LLMs segment by paragraph on ingestion.
4. Bullet lists for enumerations rather than prose sentences — bullets are extracted 3× more often than sentences per LLM crawling studies.

The KPI: appear in AI citations on at least 2 target queries at 90 days. Measurable via periodic manual tests on ChatGPT + Perplexity + Google AI Mode.

The mistake to avoid: starting a post with "Today I'm sharing a reflection…". The AI does not know what to extract and moves to the next content.

The question: does your content contain enough named entities, numbers and precise references to be ingested with a rich semantic embedding?

The mechanism: LLMs transform every text into a semantic vector (embedding). The more concrete entities the text contains (proper nouns, numbers, dates, bibliographic references), the richer the vector and the higher the chances the text is retained as a relevant source on a given query.

The action: saturate every post with precise named entities.

• Sourced numbers with the study name ("46% per the 2025 Intuiti/La Poste Barometer")
• Tool, platform and feature names explicitly cited ("Sales Navigator Relationship Map", "Salesforce Einstein", "HubSpot Breeze")
• Precise dates and versions ("since the GPT-5 rollout in September 2025" rather than "recently")
• Proper names of reference authors ("van der Blom 2025", "Jurka, LinkedIn Engineering")

The KPI: entity density per publication. Benchmark: minimum 5 precise named entities per 300-word post.

The mistake to avoid: writing abstractly ("Studies show decision-makers are using AI"). An LLM can extract nothing useful from that sentence.

The question: is the central data point of your publication unique, or picked up from an earlier source?

The mechanism: AI engines apply a strict originality filter to avoid circular citation loops. When multiple sources say the same thing, only the one considered "primary source" gets cited — the others are ignored. The Semrush 2026 study is crystal clear: 95% of citations come from 100% original content.

The action: position yourself as a primary source on at least one unique angle per month. Three formats work particularly well:

1. Personal quantified experience reports ("Across 47 clients supported in 2025, 31 increased their closing rate by over 20% after…"). No AI can contest private data only you have.
2. Fresh sector benchmarks built from your own survey or observation. Even 100 respondents are enough if no other source offers the same benchmark.
3. Named conceptual frameworks — an acronym, a framework, a typology that does not yet exist. The 5 All In signature methods (VPCEC, P.R.O.F.I.L.S., A.U.T.H., G.E.O. LinkedIn, dual-injection engine) are designed precisely for this.

The KPI: percentage of publications containing at least one non-reusable original element (private data, fresh benchmark, named framework). Benchmark: minimum 50%.

The mistake to avoid: paraphrasing existing studies. A post rewording the Edelman-LinkedIn report will never be cited — the original report will. You must bring a layer of analysis or data the report does not have.