LLM (Large Language Model)

An LLM is an AI model trained on large volumes of text that can understand and generate natural language, serving as the core engine behind AI agents and chatbots.

The first misconception many companies run into is assuming the LLM itself already knows their internal information — in reality it only holds the general knowledge from training, much like a new hire who knows the industry but not the company's internal workflows, and needs something like RAG to answer company-specific questions. That basic distinction has to be clear before evaluation even starts, or the gap gets mistaken for a system malfunction. Leading LLMs include GPT, Claude, and Gemini, and the factor most often overlooked in selection is price: within the same vendor's lineup, the gap between economy and flagship models can run over a hundredfold — OpenAI's economy tier costs about $2.4 per 10,000 conversations versus about $350 for the flagship — so picking the wrong tier can mean an annual cost gap of over NT$1M, often more worth attention up front than any performance bump. Economy models are usually plenty for support-email triage or FAQ answering; flagship tiers only earn their keep on complex reasoning, contract review, or cross-paragraph logic — 'more expensive' isn't automatically 'safer,' and matching the tier to actual task difficulty, not budget, avoids padding the monthly bill for no real benefit.