── ── AI & agents
Generative Engine Optimization (GEO)
Buyers increasingly ask an AI assistant "what should I use for X?" instead of scanning ten blue links. The assistant can only recommend brands it can recognize, verify, and cite. A brand that is ambiguous, unsourced, or absent from the third-party pages the engine retrieves is either left out of the candidate set or described wrongly — stale prices, the…
Run Generative Engine Optimization (GEO) on a real problem
Bring something you're actually deciding — free, in the browser.
How it works
Output: GEO baseline + fix list + re-test share
1. Build the prompt panel. 20–50 prompts in buyer language (category recommendations, comparisons, alternatives, problem-solving), run across 3–4 assistants. Gate: prompts come from real customer language (interviews, tickets, search terms), not self-written slogans → otherwise stop. 2. Measure the baseline. For each prompt record: mentioned or not, position, whether your own page is cited, whether the description is accurate, and which third-party sources are cited. Answers are stochastic, so report proportions across runs. Gate: ≥3 runs per prompt → otherwise draw no conclusion. 3. Fix the entity. One brand name, one category, one positioning line — identical on the website, LinkedIn, directories, Wikidata. Add Organization/Product structured data with sameAs links to those profiles. Treat this as identity hygiene, not an AI switch: Google states no special markup is required for its AI features. Gate: name and category match everywhere → otherwise fix first. 4. Create citable facts. Each core page carries extractable fact blocks: specs, price range, who it is for, comparison table, method and data sources. Gate: every fact is externally verifiable and there is no unsupported "first" or "best" → otherwise delete it. 5. Earn third-party corroboration. Inventory the sources the engines actually cite (review sites, directories, communities, press) and pursue genuine listings and reviews. No paid fake reviews, no astroturfed posts. Gate: corroboration comes from an independent third party → otherwise it does not count. 6. Cover the buyer prompts. For each panel prompt where you are absent, publish a direct-answer page (comparison, alternatives, use case, pricing). Confirm robots.txt is not blocking the AI search crawlers you want to be retrieved by (e.g. OpenAI's OAI-SearchBot controls inclusion in ChatGPT search answers). 7. Re-test and iterate. After 4–8 weeks re-run the same panel and compare share. 8. Stop-rule: share has not moved and steps 3–5 are complete → return to step 1 and check whether the prompts really represent your buyers.
Output template: prompt · assistant · mention rate · position · cites us? · description accurate? · third-party sources cited · gap (entity/facts/corroboration/content) · fix · owner · re-test date
When to use it
- user says 'get ChatGPT / Perplexity to recommend us', 'AI answers never mention our brand', 'GEO', 'generative engine optimization', 'AI search visibility', asks 'how do AI assistants describe our product?', or wants to raise how often a brand is mentioned and cited in generative-AI answers
When not to use it
When the decision is routine and reversible, applying a formal method costs more than it returns.
Worked example
GEO-bench — the Visibility Experiments That Named the Field (2023–2024)
In November 2023 a team from Princeton University and IIT Delhi (Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan, Deshpande) posted "GEO: Generative Engine Optimization" to arXiv; it was published at KDD 2024. It is the first public, controlled test of the question this skill answers: what makes a source more likely to be used and cited in a generative engine's answer? It is a research experiment, not a brand campaign, so it exercises some steps of the Process rigorously and does not test others. Both are worth…
Install this skill (free, MIT)
npx skills add deciqAI/knowledge-skillsUseful? Star the repo — stars help other builders find it.
Related mental models
When an agent takes over part of a job, the job does not disappear — a bundle of tasks changes hands.
When AI replaces human work, the customer needs fewer seats — so seat pricing moves in the opposite direction from the value delivered.
In the AI era, models and compute can be bought.
Answer engines — featured snippets, "People Also Ask" boxes, voice assistants, AI answer panels — lift one passage and present it as the answer.