An LLM (Large Language Model) is an AI model trained on large volumes of text (billions to trillions of tokens) that learns to generate and interpret natural language with quality. The explosion of interest since 2022 with ChatGPT, Claude, Gemini and others reflects significant leaps in capability — and also in risks that require careful governance.
Corporate applications of LLMs include: content generation and summarisation (articles, reports, emails), knowledge assistants over internal data (via RAG), automation of interactions (improved chatbots), translation, extraction of information from long documents. In each, the implementation decision involves trade-offs — privacy (external models vs on-premises infrastructure), cost (API calls add up), quality (each model has different strengths), and governance (hallucinations are real, human oversight is necessary).
INFOS adopts LLMs with judgement. Where they add obvious value (assisted content generation in AutomationForge, document summarisation in Document Management), they are implemented with clear guardrails — audited prompts, reviewable outputs, usage logs. INFOS does not use LLMs for automatic decisions about people or for interfaces where a hallucination could cause material harm.