A large language model (LLM) is an AI model trained on vast amounts of text to understand and generate human-like language by predicting the most likely next words in a sequence. Models like Claude and Gemini are LLMs, and they power most modern conversational and agentic AI.
An LLM is powerful but, on its own, ungoverned. It predicts plausible text, which means it can be fluent and wrong at the same time. That gap between fluent and correct is exactly why shipping an LLM straight into customer conversations without guardrails is risky.
LLM is a foundational industry term, not Aide-owned. Aide, the agentic AI platform for customer experience, runs on leading LLMs (Claude and Gemini, on zero data retention terms) but treats the raw model as an input, not the product. The product is the intent-first architecture and the verified procedures wrapped around the model, not the model itself.
Guardrails are the point. Automation is scoped by intent, written into verified procedures, and tested before anything goes live, so fluent-but-wrong answers do not reach customers. The team can read the logic around the model, which keeps their understanding of how resolutions happen growing rather than atrophying behind it.
Frequently asked questions
- What is an LLM in simple terms?
- It is an AI model trained on huge amounts of text that generates language by predicting likely next words. It can write and converse fluently, but fluency is not the same as accuracy.
- Is a large language model enough to run AI customer support safely?
- No. An LLM is a capable input, but on its own it can be confidently wrong. Safe automation requires guardrails: intent scoping, verified procedures, and testing before deployment.