This section visually explains how LLM predicts language, why it might make mistakes, and how to use it appropriately.
LLM is a model trained on large volumes of text to predict the next part of a language sequence. Through the iterative prediction process, the model can generate paragraphs, summaries, and answers to questions very naturally.
Tokens and context windows
The model doesn't read text like a human; instead, it processes units called tokens. The context window is the amount of information the model can view at one time. Content that is too long or lacks structure can cause important information to be missed.
Why might LLMs give incorrect answers?
The fundamental goal of the model is to generate a plausible sequence, not to automatically verify facts. Even with a lack of data, the model can still produce a seemingly convincing answer. This phenomenon is often referred to as hallucination.
What is LLM suitable for?
LLMs excel at drafting, categorization, formatting transformation, summarizing, and suggesting. In legal, medical, financial, real-time data, or critical decision-making environments, reliable sources and expert review are always essential.
Three principles of use
Provide sufficient context, require the model to clearly state any uncertainties, and double-check key facts. Do not enter sensitive data without organizational permission.
