AI Research
Sep 7, 2026
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2 min read
LLM experience gain flips the question: What can an LLM do for the person already on your page?
1 min read
A model that leads this quarter can slip in the next, so the choice is rarely permanent.
For a growth team, microagents 10x repetitive work without waiting for one do-everything tool.
LLM prompts are longer and more specific than keywords, so relevance is judged against a hyperspecific query, often at the passage level.
A static pretrained model can confidently state wrong or outdated facts about your brand, category, or pricing.
Sep 6, 2026
Higher rankings raise your citation odds, but many AI answers cite nothing at all, so visibility no longer guarantees a click.
Aug 21, 2026
Agentic commerce (also referred to as agentic shopping) transforms organic search from a source of cheap traffic into the mandatory gatekeeper of AI verification.
Counting only the citations you can see undercounts how often AI systems actually use your material.
If your page isn't cited, you're invisible in the AI answer even when you rank in classic results.
Brand search volume is the single biggest predictor of how often AI engines name you, ahead of anything on your site.
Self-host an open source LLM and the hallucination risk becomes yours to measure and contain.
Open weight models give you direct control over LLM capability, but the same hallucination and citation risks still apply.
Jul 6, 2026
An embedding is the representation your content competes as inside an AI engine, and identical text can produce different embeddings across models.
Top-k retrieval makes AI citation a zero-sum game with far less surface than 10 blue links, often fewer than 10 chunks per query.
LLMs can make things up in a very convincing way, and being the well-grounded, clearly attributed source is your best defense.
Traditional personas are descriptive; synthetic personas are predictive. One documents a segment, the other simulates it.
The cutoff score that decides whether a chunk is close enough in meaning to a query to be retrieved or cited at all.
Chunks are the retrieval unit, not pages, so a long pillar page competes as many fragments and its weakest paragraph can disqualify its strongest.
Phrasing content in the same meaning-space and words your audience and the model use, so it gets retrieved for the right queries.
The question shifts from "does my page contain the query" to "does my page sit close to the query in meaning-space."
Bard was the first big opportunity for a Google competitor to take share, and it launched with a stumble that cost Alphabet $100B.
Turn 1 is the first prompt and answer in an AI conversation, and it is often the most valuable moment because many users stop there.
Ask the same person the same question 2 weeks apart and they're about 85% consistent with themselves.
Prometheus gave Bing its first real shot at gaining more than its 6% market share by pairing the Bing index with a large language model.
SGE was Google's first iteration of AI-generated answers inside Search, and it pushed the whole SEO industry to learn new skills.
