LLM
An LLM, or large language model, is an AI model trained on large amounts of data that can process and generate language. It is one of the building blocks of today’s AI chatbots.
Model vs. chatbot
A chatbot is the application you talk to. It can use a language model, a search engine and other tools. That’s why an app’s answer doesn’t have to come only from what the model learned during training.
Why do answers vary?
An answer depends on the wording of the question, the earlier conversation, the available sources and how the app works. A model can also produce information that sounds credible but is wrong.
In Heuriso we compare the saved answers of each service. We don’t assume that all chatbots see a brand the same way.
Why can a model get facts about a company wrong?
A language model doesn’t store every fact as an approved record. It generates an answer from learned patterns and the context it receives. So it can mix up data about companies with similar names, attribute a product to the wrong vendor, or present an old offer as current. Web search can help, but even then the quality of what it finds has to be judged.
For a brand owner, this means publishing unambiguous information. Your company name, domain, range of services and terms should be consistent with each other. This isn’t about writing artificially for a model; it is about removing ambiguity that confuses people too. It’s worth checking whether similar product or company names lead to mix-ups in the answers.
Keep the model’s knowledge separate from documents provided in the current conversation. If a user attaches your company’s offer, the answer can reflect it without any change to the model’s training. That result shows the model working with supplied material, not spontaneous knowledge of your brand.
Common mistake: equating the model with the whole service
A change in a chatbot’s answer doesn’t necessarily mean the model was retrained. The app may have used different search results, changed its instructions or picked a different tool. Conversely, fixing your website doesn’t automatically update every model’s knowledge. In monitoring, record the service, the prompt and the date of each answer instead of attributing every change to one mechanism.
Example
One chatbot may name a company based on what the model already knows, while another finds a current roundup online and suggests a different list.
Key takeaway
A fluent, confident answer is not proof that it is correct. Check important information against sources.