A marketer’s glossary for the AI era: from LLMs to SoGV in simple terms

The lie invented by an algorithm: Why marketers must learn the language of AI

The shift from SEO optimization to managing AI-generated responses is one of the most significant changes taking place in the marketing ecosystem today. Traditional search engines presented a list of links - the choice belonged to the user. Conversational assistants go further: they formulate a ready-made answer, name a specific brand, describe a product, suggest a price. And they do all of this without asking the communications department for permission.

For branding specialists, this represents a new category of risk. A brand may have no control whatsoever over what a model says about its offering - even when the information is incorrect.

A real-world scenario: A non-existent promotion in an assistant's response

Imagine a user asking an AI assistant: "Does company X currently have a promotion on its flagship subscription?" The model responds with confidence: yes, there is a 30 percent discount for new customers available until the end of the month. Except that promotion never existed.

The consumer arrives at the company's website with a ready-made expectation - and obvious disappointment. Some will file a complaint. Some will leave, concluding the brand is unreliable. None of them will think it was an algorithm's error. In their perception, the company made a promise and failed to keep it.

This is not a hypothetical scenario. It is a pattern of reputational risk that is becoming increasingly common as AI assistants step into the role of the first point of contact for brand information. Understanding where such errors come from - and how they can be influenced - begins with mastering a handful of key technical concepts.

The foundations of generation: LLMs and the causes of hallucination

Large Language Models (LLMs) are artificial intelligence systems trained on vast collections of text - articles, websites, documents, discussion forums. Understanding their mechanics is not a luxury reserved for engineers; it is a prerequisite for consciously managing brand narrative.

LLMs: A powerful autocomplete engine

Put simply: a large language model is a highly sophisticated statistical engine. Its purpose is not to understand a question in any human sense, but to predict which word should follow the previous one - based on patterns observed in training data.

The analogy is close to everyday experience. A smartphone keyboard suggests the next word in a text message - and does so sensibly, because it has "learned" typical sequences from previous messages. An LLM operates on the same principle, only at a scale millions of times larger and with incomparably richer context. The model does not check whether a sentence is true. It evaluates whether it sounds plausible - and that is both its strength and its weakness.

Where do hallucinations about brand information come from?

Hallucination in the context of AI refers to a situation in which the model generates information that sounds confident and coherent but is factually untrue. This is not about deliberate deception - the model simply combines fragments of text from training data in a statistically plausible but factually incorrect way.

Consider this example: two companies with similar names operate in the same industry. Articles about both appeared in the training data. The model may assign the characteristics of one to the other - because both sets of information were statistically close. For the algorithm, this is a coherent response. For the PR department of one of those companies, it is a serious communications problem.

Importantly, pure language models have no built-in fact-checking mechanism. The model does not consult current databases, does not verify dates, and does not compare its responses against official sources. It generates - and nothing more.

SoGV: The new currency of visibility in the AI ecosystem

Share of Generative Voice (SoGV) is a metric that measures how often and in what context a brand appears in responses generated by AI models - relative to the competition. In other words: it is the AI equivalent of market share, only measured not in sales but in algorithmic recommendations, tracked by observing which questions and topic categories cause a brand to appear in generated responses.

For marketing directors working in a corporate environment, SoGV is becoming a first-order metric - one that complements, and in many cases replaces, the classic search ranking position indicators. Simply counting clicks no longer answers the question of whether a brand is perceived as credible when a consumer poses a question to a conversational assistant. It is worth noting that SoGV measures precisely this share of narrative - brand presence in generated responses - but does not by itself determine the quality of an offering or the truthfulness of every mention.

From Search Engine Position to Share of Recommendations

SEO (Search Engine Optimization) rested for years on one simple assumption: a high position in the results list means visibility. The user sees ten links and clicks the one that looks best.

An AI assistant eliminates that list. Instead of links, the user receives an answer: "For your use case, product Y is the best fit, because..." There is no room for second and third place. There is a first recommendation - or there is absence.

The difference is fundamental. SoGV measures precisely this share of narrative: how often a brand lands in that first recommendation, in which question categories, with what surrounding context, with what emotional connotation. It is not a derivative of SEO. It is a new analytical discipline.

Reputation protection: Entities, parsing, and RAG systems

Knowing that hallucinations exist is only the starting point. The practical question is: what can branding teams do to increase control over how AI talks about their brand? The answer lies in three interconnected concepts: entities, parsing, and RAG systems.

Entities: Meaning matters more than keywords

An entity in algorithmic terms is a specific, identifiable object - a person, an organization, a product, a place - described by a set of precise attributes and relationships. It is not simply a word.

Consider the comparison: the word "apple" can mean a fruit, a technology giant, or the Beatles' record label. The entity "Apple Inc." is a company headquartered in Cupertino, the manufacturer of the iPhone, listed on NASDAQ, associated with a specific ecosystem of products and services. Models build networks of relationships between such precise objects - and draw conclusions about a brand on that basis.

For branding specialists, this carries a practical implication: the more clearly a brand is described in the digital space as a coherent entity - with an unambiguous category, attributes, and relationships to other entities - the lower the risk that a model will confuse it with a competitor or fill in the gaps with its own incorrect speculation.

Parsing: A technical barrier for algorithms

Parsing is the process by which a machine reads the code of a website and organizes it into a structure that an algorithm can understand. In other words: it is the technical foundation of content indexing, and it determines whether information about a brand reaches the model at all.

The problem is that algorithms only see what they can read. Text embedded as an image - for example, a banner displaying current prices or an infographic describing an offer - is invisible to a parser. Tables without proper semantic markup, content loaded dynamically through JavaScript, headings used purely for visual purposes - all of this creates barriers through which key brand information never reaches the models.

The technical readability of a website is not an IT detail. It is a necessary condition for presence in the AI ecosystem. A site whose content an algorithm cannot parse effectively does not exist for the model generating the response.

RAG: An open-book exam for artificial intelligence

RAG (Retrieval-Augmented Generation) is an architecture in which a language model does not rely exclusively on knowledge memorized during training. Before generating a response, the system first searches an external database - current, reliable, and controlled - and only then formulates the text.

The educational analogy works particularly well here. A traditional exam requires memorizing everything; the test-taker guesses and may be wrong. An open-book exam allows consulting notes: the answer is more precise because it is based on a verified source rather than reconstructed from memory.

For companies using RAG, this translates into a measurable benefit: instead of hoping the model correctly remembered content from a website, it is possible to supply it with current, structured information directly. An up-to-date price list. A current product offering. The active terms of a promotion. The system retrieves the appropriate data and generates a response on that basis - dramatically reducing the space in which a hallucination might appear.

Glossary at a glance: Consciously designing brand visibility

Each of the concepts discussed addresses a different piece of the same problem. LLMs explain why a model may say something untrue - because it operates on probability, not verification. Hallucinations describe the consequence of that mechanism in the context of brand information. Entities indicate how knowledge about a company should be organized so that an algorithm can unambiguously identify and connect it. Parsing determines whether that knowledge is technically accessible at all. RAG presents the architecture that replaces a model's statistical guesswork with current, controlled data.

SoGV closes this chain on the analytics side: it is a measure of whether the actions taken are producing results - whether the brand actually appears in generated responses, in what context, and how frequently.

For quick reference - all concepts at a glance:

• LLM (Large Language Models) - AI systems that predict text based on statistical patterns; they do not verify the truthfulness of their responses.

• Hallucination - information generated by a model that sounds credible but is factually untrue.

• Entity - a specific, identifiable object (brand, product, company) described by precise attributes and relationships in the digital space.

• Parsing - the process by which an algorithm reads a website's code; content that is not technically readable does not reach AI models.

• RAG (Retrieval-Augmented Generation) - an architecture that connects a language model with an external database, reducing hallucinations through access to current sources.

• SoGV (Share of Generative Voice) - a metric measuring the frequency and context in which a brand appears in AI-generated responses, relative to competitors.

Mastering this terminology is not an end in itself. It is the entry point to consciously designing brand presence in an environment where the first response to a consumer's question is no longer formulated by a search engine, but by an algorithm. Technical content clarity - the structured description of entities, correct parsing, data accessibility for RAG systems - is today the most durable foundation of communicative credibility in the corporate digital ecosystem.