Why a Language Model Lies with Full Conviction

The technical committee approved the deployment of an AI system for outbound communication. Emails are sent regularly, and the process appears automated and efficient. However, nobody knows that one in every dozen communications contains erroneous technical data, which sounds like an expert recommendation. Agencies send emails that sound like a primitive bot, which our clients—tough engineers and maintenance managers—immediately send to spam. The problem is not that the email sounds like a bot. The problem is that it sounds like an expert who is wrong.

The high-profile case of a lawyer who cited non-existent rulings generated by ChatGPT in court shows the scale of the threat. The model presented fictional judgments with full certainty, and the lawyer referred to them in an official procedural document. If an AI system can generate non-existent court judgments with such certainty, it will generate erroneous technical product parameters, non-existent standard certificates, or fabricated reference data in an email to a maintenance manager with equal certainty. How many such emails has your system already sent before anyone noticed?

The Mechanism of Token Generation as the Source of the Problem

A language model possesses no internal state of knowledge or ignorance. Each token is generated solely based on a probability distribution obtained through a softmax function from logits, without any factual verification against reality. A logit is a raw score from a neural network, and softmax normalizes these values to form a probability distribution that sums to one. A high probability for a token merely indicates statistical coherence with training data, not adherence to reality, which structurally prevents the model from distinguishing fact from fabrication.

In practice, this means that the certainty of the AI's answer does not correlate with its correctness. When the model generates a sequence, it calculates full distributions for the entire vocabulary and selects tokens according to a decoding strategy. Greedy decoding chooses the token with the highest probability at each step. Beam search explores multiple paths simultaneously, and temperature as well as top-k and top-p parameters control the model's creativity level. Each of these mechanisms directly influences the risk of hallucinations. The model calculates full distributions for the entire vocabulary at each generation step—this is the only internal signal it possesses, and there is no information in it about alignment with reality.

Even when the user requests a verbalization of the confidence level, this declaration stems from an optimization of linguistic style, not from an actual calibration of knowledge, making it an unreliable indicator of risk. In classical statistics, model calibration means that the declared confidence level reflects the actual frequency of correct answers—if a system states 90 percent confidence, it is right in 90 out of 100 cases. This is the standard that every expert system with uncertainty signaling strives for. In LLMs, verbalized certainty depends on how the question is asked, is highly dependent on the prompt, and requires careful engineering to have any meaning as a risk indicator.

What RLHF Optimization Entails

Optimizing the RLHF (Reinforcement Learning from Human Feedback) process in LLMs relies on more efficient preference data collection and algorithm stabilization. This involves libraries such as TRL (Transformer Reinforcement Learning) and PEFT structures with LoRA techniques, which allow the process to run in multi-card environments (e.g., using OpenRLHF).

🐶 The simplest analogy: > This works exactly like training a young puppy. When you give a dog the command "sit," and it sits—you give it a treat. The puppy quickly learns that good behavior is rewarded. In RLHF, "treats" for the robot are points and praise from humans! The Impact of RLHF on Response Tone RLHF optimization leads models to adopt a firm and helpful tone even with high probability distribution entropy, masking the lack of factual verification. A model that internally hesitates between several tokens with similar probabilities still constructs a sentence that sounds like a confident expert recommendation—because it has been taught by RLHF to do so, because humans evaluating responses preferred a confident tone over a hesitant one. This effect increases the probability that incorrect data will reach tough B2B recipients and immediately go to spam.

The lack of an internal know/don't know state means the model produces incorrect answers with the same certainty as correct ones, exposing companies to legal claims and reputational loss before the error is detected. I have seen this repeatedly in tests—the model generates an incorrect technical parameter with the same confident tone as a correct one. The difference is not visible in the text—it is visible in the token distribution. Classical expert systems based on rules and knowledge bases had a built-in mechanism for signaling ignorance—if a query went beyond the scope of the knowledge base, the system would respond with "no data" or "outside scope of competence." This was a fundamental architectural element, not an optional feature. LLMs do not have an equivalent mechanism—for every prompt, they generate an answer, unless explicitly designed not to.

Research on subsequence associations shows that hallucinations are not random but result from predictable patterns in the token generation mechanism. Decoder transformers can be viewed as models embedding subsequences—when a given subsequence has strong associations with a specific type of response in the training data, the model follows that association, ignoring the rest of the context. If, in the training data, the name of a specific technical standard frequently co-occurs with certain parameters, the model may append those parameters to the answer, even if the question concerns a different version of the standard or a different product. The SAT algorithm achieves a 26 percent higher hallucination reproducibility rate than classical attribution methods, identifying specific subsequences that trigger errors in model responses. These trigger subsequences systematically lead to hallucinations in various contexts, which explains why traditional heuristics based on single probabilities fail. Hallucinations are not random noise that can be averaged out by sending many emails—they are a systematic error caused by specific patterns in the input text.

Full Sequence of Distributions as a Control Tool

The full sequence of token distributions, referred to as the LLM Output Signature, can be used by independent detectors to identify hallucinations with higher effectiveness than simple heuristics based on single probabilities. A simple heuristic only looks at the probability of the selected token at a given step—this is like judging the quality of an engineering decision solely by its outcome, without looking at the process. LOS looks at the full sequence of distributions for all generation steps—both the probabilities of actually selected tokens (ATP) and the shape of the entire distribution at each step (TDS). Attention models (LOS-Net) treat this sequence as input data and learn to recognize patterns characteristic of hallucinations—analogous to how an IDS system in cybersecurity doesn't look at a single packet, but at a network traffic pattern. This mechanism points to a specific place in the architecture where control can be introduced without modifying the model itself. When I was building the architecture for AGAPE's outbound communication system, the first question was—where in the data flow can we check if the model knows what it is generating? The answer lies in the full sequence of token distributions—not in the answer text itself.

Architectures based on external certainty signals and human-in-the-loop allow controlling the risk of hallucinations at the data flow level, rather than within the neural network weights. Such a setup provides the CTO with a starting point for architectural decisions that limit legal liability. An organization that analyzes the full sequence of token distributions and gates responses before sending them to a client controls the risk of hallucinations at the data flow level—without modifying the model itself and without engaging internal development teams.

Consequences for SME Implementations

Implementing AI without validation generates erroneous outbound data to clients, damages brand reputation, and exposes the company to legal liability. Legal risk includes incorrect information in offer materials and outbound communication, which can form the basis of claims. The EU AI Act introduces high-risk classification and documentation requirements for AI systems in specific applications. GDPR risk arises when automated processing of personal data in outbound communication requires transparency and the ability to challenge decisions. An AI system generating emails without validation may violate the data minimization principle and the DPIA requirement. Reputational risk means that a tough engineer or maintenance manager who receives an email with incorrect technical parameters will not only send it to spam but will remember the sender as a source of unverified information—and this association is difficult to reverse. Regulatory pressure from GDPR and the EU AI Act further reinforces the need to understand that language models in B2B do not signal uncertainty when they don't know. Corporate data integrity, IT infrastructure security, and data privacy policies become directly dependent on the ability to distinguish statistical coherence from factual correctness.

Starting Point for Architectural Decisions

Understanding why a model produces errors with full conviction allows for precisely identifying where in the architecture this phenomenon can be controlled. The technical committee stops asking if AI makes mistakes and starts asking where in the data flow we can check this—this is a change in perspective that translates into concrete architectural decisions. A CTO who understands where the problem lies in the architecture can decide to gate responses without involving internal developers—and without modifying the model itself.

Describe in the comments a situation where an AI system generated a response that sounded more convincing than it was correct—an incorrect technical parameter, a non-existent source, a fabricated reference. You don't have to provide the system or client name. Each such situation is a specific pattern—and patterns are predictable, as shown by the SAT algorithm with a 26 percent higher hallucination reproducibility rate than classical attribution methods. The more patterns collected from real operational environments, the more precise the assessment of implementation risk. If you want to assess the risk in your specific environment—start by describing one case.