IT Glossary
What is an LLM?
Large Language Model: the engine behind ChatGPT and its peers — AI trained on vast text that understands and generates language.
The technology that moved AI from research labs into every office has a technical name: LLM — large language model. The mechanics, reduced to essence: trained on immense quantities of text, the model learns the patterns of language so deeply that, by forever predicting “the next word”, it ends up drafting, summarizing, translating, extracting information, writing code and holding coherent conversations — ChatGPT, Claude and Gemini being the commercial faces of this machinery. For a company, LLMs are engines, not finished products — the value appears at assembly: connected to your documents (the RAG technique — the model answers from your procedures, quotes and history rather than the internet's memories), wired into workflows (the client email classified with a reply drafted, the scanned invoice turned into a record, the weekly report generated from data), or as assistants for the language chores that eat your staff's day. The limits matter as much as the powers: an LLM doesn't “know” — it models the plausible, so it can hallucinate false details in a perfectly confident tone; arithmetic needs checking; and its knowledge has a cutoff date. Hence the rules of sound assembly: answers anchored in your own content, a human in the loop where stakes are real, and sensitive data only through business tiers with contractual guarantees — never through free personal accounts.
Let’s talk about your project
Message us on WhatsApp or send an email — you talk directly to a developer.
office@northdan.com · +40 752 070 247
Why it matters for your business
Language chores, compressed
Drafting, summarizing, translating, pulling data out of documents — the company's reading and writing hours shrink to review time.
Company knowledge, queryable
Connected to your documents through RAG, the model answers from your procedures and history — the archive becomes a colleague who knows everything.
Laboratory power at subscription prices
Frontier models are used through APIs at pay-per-use rates — capabilities that five years ago existed at no price at all.
Frequently asked questions
How does an LLM differ from a classic chatbot?
The classic chatbot follows scripts written by people — it spots keywords and replies from lists; an LLM understands free phrasing and generates new answers. That's why the old bots frustrated users with “I didn't understand the question” while LLM-based ones converse naturally — with their own flip side: they can say unplanned things, so a serious implementation anchors them in controlled content.
What is RAG — the term in every AI proposal?
Retrieval-Augmented Generation: before answering, the system searches your documents for the relevant passages and hands them to the model as source material — the answer comes from company content, with hallucinations sharply reduced and sources citable. It's the standard architecture for corporate chatbots and internal search — and the difference between real “AI on your data” and marketing.
Is it safe to use LLMs with our customers' data?
With the right editions, yes: business and enterprise plans of the major providers don't train on your data (a contractual guarantee — to be read, not assumed), EU hosting exists, and for high sensitivity there are open-source models run on your own infrastructure. The real risk is informal: employees pasting confidential material into free personal accounts — solved with written rules and company accounts.
Let’s talk about your project
Message us on WhatsApp or send an email — you talk directly to a developer.
office@northdan.com · +40 752 070 247