Why does AI make things up about my brand?

Because a model predicts plausible text rather than looking facts up, and thin coverage produces invention instead of silence. The fix is rarely a correction request and usually more material for the system to be right from.

Why does the same question give different answers each time?

Three reasons at once: the model samples among plausible next words, the searches it generates vary, and any memory it carries changes the input. Measure how often you appear across repeated fresh sessions, never whether you appeared once.

Why does AI recommend my competitor instead of me?

Work out which of five filters you fail before choosing a fix. Absent from category questions but present when named is a category association problem. Retrieved but never used is usually structure. Used but credited to someone else is citability.

Does longer content help or hurt with AI search?

Depth still wins and padding now costs you. Retrieval finds more material than fits, so most candidates are discarded before merit is assessed, and a page whose answer is spread across two thousand words is harder to extract than one where it is not.

Is a mention without a link worth anything?

Yes, and it is invisible in your analytics. Four different outcomes get called a citation: unattributed use, a brand mention, a named source, and a clickable link. Measuring only the last counts the rarest and treats the other three as zero.

What makes a page more likely to be cited?

One claim per passage, a visible date, a stated source including when the source is you, a stable URL, and an honest scope. The rule underneath all five is that a careful editor could repeat it without doing additional research.

03Module

How LLMs Process Information

How AI models understand, retrieve, and synthesize information to generate responses. Understanding this helps you optimize content for AI comprehension.

Available3 lessons20 min

A model predicts text rather than looking facts up, its own knowledge is frozen at a training cutoff, anything current has to be retrieved, and most retrieved material is discarded before your page is judged on merit. Recommendation is then a sequence of five filters: interpretation, retrieval, selection, synthesis, attribution. You are eliminated at the first one you fail, and the fix at each is different.

This is the module that explains why the advice in this course is what it is. Four mechanics: a model predicts text rather than looking facts up, its own knowledge is frozen at a training cutoff, anything current has to be retrieved, and the retrieved material is mostly discarded before your page is judged on merit.

From those four, the module builds the sequence a system actually works through before it names anyone: interpretation, retrieval, selection, synthesis, attribution. You are eliminated at the first step you fail, and the fix at each step is different. A quarter spent improving content while the failure is at retrieval is a diagnosis problem rather than an effort problem.

It closes on citations, and on the uncomfortable fact that three of the four ways you can be credited are invisible in your analytics.

If you read one lesson: How AI Decides What to Recommend. It contains the diagnostic that tells you which step is failing for you.

Lessons

Questions this module answers

Why does AI make things up about my brand?: Because a model predicts plausible text rather than looking facts up, and thin coverage produces invention instead of silence. The fix is rarely a correction request and usually more material for the system to be right from.; In fullLLM Basics for Marketers
Why does the same question give different answers each time?: Three reasons at once: the model samples among plausible next words, the searches it generates vary, and any memory it carries changes the input. Measure how often you appear across repeated fresh sessions, never whether you appeared once.; In fullLLM Basics for Marketers
Why does AI recommend my competitor instead of me?: Work out which of five filters you fail before choosing a fix. Absent from category questions but present when named is a category association problem. Retrieved but never used is usually structure. Used but credited to someone else is citability.; In fullHow AI Decides What to Recommend
Does longer content help or hurt with AI search?: Depth still wins and padding now costs you. Retrieval finds more material than fits, so most candidates are discarded before merit is assessed, and a page whose answer is spread across two thousand words is harder to extract than one where it is not.; In fullHow AI Decides What to Recommend
Is a mention without a link worth anything?: Yes, and it is invisible in your analytics. Four different outcomes get called a citation: unattributed use, a brand mention, a named source, and a clickable link. Measuring only the last counts the rarest and treats the other three as zero.; In fullThe Role of Citations and Sources
What makes a page more likely to be cited?: One claim per passage, a visible date, a stated source including when the source is you, a stable URL, and an honest scope. The rule underneath all five is that a careful editor could repeat it without doing additional research.; In fullThe Role of Citations and Sources

Lessons

Questions this module answers

Why does AI make things up about my brand?: Because a model predicts plausible text rather than looking facts up, and thin coverage produces invention instead of silence. The fix is rarely a correction request and usually more material for the system to be right from.; In fullLLM Basics for Marketers
Why does the same question give different answers each time?: Three reasons at once: the model samples among plausible next words, the searches it generates vary, and any memory it carries changes the input. Measure how often you appear across repeated fresh sessions, never whether you appeared once.; In fullLLM Basics for Marketers
Why does AI recommend my competitor instead of me?: Work out which of five filters you fail before choosing a fix. Absent from category questions but present when named is a category association problem. Retrieved but never used is usually structure. Used but credited to someone else is citability.; In fullHow AI Decides What to Recommend
Does longer content help or hurt with AI search?: Depth still wins and padding now costs you. Retrieval finds more material than fits, so most candidates are discarded before merit is assessed, and a page whose answer is spread across two thousand words is harder to extract than one where it is not.; In fullHow AI Decides What to Recommend
Is a mention without a link worth anything?: Yes, and it is invisible in your analytics. Four different outcomes get called a citation: unattributed use, a brand mention, a named source, and a clickable link. Measuring only the last counts the rarest and treats the other three as zero.; In fullThe Role of Citations and Sources
What makes a page more likely to be cited?: One claim per passage, a visible date, a stated source including when the source is you, a stable URL, and an honest scope. The rule underneath all five is that a careful editor could repeat it without doing additional research.; In fullThe Role of Citations and Sources