LLM (Large Language Model)

A large language model, put simply, always predicts the next piece of text. That usable answers, translations, and summaries emerge from this is a consequence of the training scale - not of the model understanding anything in a human sense or looking anything up.

The familiar limits follow directly from how it works. A model has a knowledge cut-off at training time and knows nothing that came after it or was never public. It can invent statements that sound right. And it has no sense of its own uncertainty - a wrong answer sounds exactly as assured as a correct one. So applications that need to hold up are given context from your own data and a checking stage in front.

When choosing, the leaderboard a vendor shows rarely decides it. What matters: how well does the model solve precisely your task, measured against real cases from your business? What does one run cost, projected onto actual volume? Where is the data processed? And how much work would switching be - because the market moves faster than projects run.

So we build the integration to be replaceable. Which model sits behind a feature should be a configuration decision, not an architectural one. That also makes it possible to use expensive models only where they make the difference and handle routine cases more cheaply.