If you have ever seen AI models before installation, you have probably come across something like: “mistral-7b-instruct-v0.3.Q4_K_M.gguf”. The model name may ring a bell, we have already explained the meaning of the letter “b”, but what is that string of letters and numbers at the end?… That is quantization. A simple but clever technique that…
Word, PDF, Excel: what really happens behind the scenes You may have tried to “give a document to the AI” and wondered: does it actually read it? Does it understand my Excel spreadsheet? Does it see the layout of my Word contract? The short answer: no, not directly. And understanding why means understanding how artificial…
What really happens when your AI thinks When talking about computer performance, the usual reflex is to look at the processor: “how many GHz? how many cores?” This is logical for many tasks — video editing, gaming, running a complex spreadsheet. But for running a language model like those used by ArkeoAI, this logic is…
It is the basic unit an AI uses to read, understand, and write. You may have noticed that AI services talk about “tokens”: token limits, cost per token, context window in tokens… The word appears everywhere, but it is rarely explained clearly. Yet understanding what a token is means understanding how a language model perceives…
Understanding hallucinations in language models You may have already heard the term “hallucination” applied to artificial intelligence. A model that cites a law article that does not exist. An assistant that calculates a redundancy payment at €107,000 when the real amount is €2,625. A fabricated case law reference, written with the same confidence as a…
Concrete use cases, without excessive promises An accountant spends an average of 30 to 40% of their time on low-value-added tasks: documentary research, repetitive regulatory checks, formatting standard responses. AI does not replace expertise — but it can absorb a large part of this burden. Instant regulatory research Instead of manually browsing the General Tax…
It’s not a technology problem Studies keep multiplying: between 60 and 80% of corporate AI projects fail to meet their objectives. Yet the technology has never been more accessible. So where is the problem? Mistake #1: believing AI does everything on its own AI is a tool, not an autonomous employee. It amplifies what you…
Demystifying a phrase everyone uses but few truly understand “Offline artificial intelligence”, “local AI”, “on-premise model”… These expressions are increasingly common, but their concrete meaning remains unclear for many professionals. Here is what they actually mean. Online vs offline: the fundamental difference An online AI tool operates on remote servers. When you ask a question…
What you send to the cloud no longer truly belongs to you When a lawyer, notary, or accountant uses an online AI tool, something happens that many people are unaware of: the data entered — questions, document excerpts, client names — passes through external servers, often located outside the European Union. What the GDPR says…
When size doesn’t tell the whole story Billions of what, exactly? The “B” stands for “billions” — and it refers to the number of parameters in the model. A parameter is, in simplified terms, a connection learned during the model’s training on billions of texts. The more parameters, the more general knowledge the model has…