Nexuldra
// ai
Large Language Model (LLM)
A large language model (LLM) is a neural network trained on massive text collections for one task: predict the next word. From that training it acquires grammar, facts, reasoning patterns and multiple languages — enough to power chatbots, copilots, search and agents. It predicts plausible text, not verified truth, which is the root of both its strengths and its failures.
LLMs matter because one component now covers dozens of language tasks that used to require separate systems: drafting, summarising, translating, classifying, extracting data from documents. A single well-deployed model can absorb the repetitive written work of a team — support replies, report drafts, ticket triage — at fractions of a cent per use. The discipline is to use them where a wrong answer is cheap or checkable, and to anchor them to your own documents when facts matter.
A concrete example: a support desk routing incoming emails by topic and urgency, then drafting a first reply from the relevant manual page. A few hundred emails a day costs a few euros a month in model calls — the API price of a strong model sits in the single-digit euros per million input tokens at the time of writing, and it falls almost every quarter. The real work is everything around the model: the prompt, the retrieval, the evaluation set, the guardrails.
Frequently asked questions
- Is a bigger model always better?
- For narrow business tasks — classifying, extracting, answering from your documents — smaller, cheaper models often match large ones, especially with good prompting or light fine-tuning. The gap shows on broad reasoning and rare knowledge. Benchmark candidates on your own tasks before paying for capacity you will not use.
- Does an LLM know our company?
- Only what your prompts and retrieval give it at question time. Its training data froze months or years ago and contains nothing about your internal documents, prices or processes. That is exactly the gap RAG fills: the model reasons, your documents supply the facts.