#Grounding

記憶、狀態與知識：別再把所有東西都叫做「記憶」

一個為中型旅行社打造的旅遊規劃 Copilot，被問了一個很直接的問題：「這間飯店之前是否在輪椅使用者的無障礙審查中未通過？」

以 RAG 進行基礎化：AI 系統如何在回答之前檢索佐證

大型語言模型之所以有用，是因為它們能用流暢的語言綜合、解釋和轉化資訊。但一旦我們要求它們處理即時的、私有的，或需要可驗證依據的資訊，它們就變得不可靠。模型在訓練期間可能看過類似的素材，但這不代表它能存取當前任務需要的那份飯店合約、無障礙稽核或客戶回饋記錄。

AI 驅動的客戶支援 — 從聊天機器人到智慧型系統

客戶支援是很適合收束基礎概念的範例，因為一則訊息可能同時需要檢索、工具使用、記憶、路由和核准邊界。

為什麼 LLM 需要幫助 — 幻覺、Grounding，以及系統設計的必要性

大型語言模型能產出流暢又自信的文字。而這份自信，正是問題所在。模型可以把一筆已經下架的房源、一個上一季才變動的稅率、一則三年前的學校評分，講得頭頭是道。它沒有任何機制去查核——本來就不是為查核設計的。它的工作是根據訓練資料預測下一個最合理的 token，而合理不等於正確。

Memory, State, and Knowledge: Stop Calling Everything "Memory"

A travel-planning copilot for a mid-size agency is asked a straightforward question: "Did this hotel fail an accessibility review before for wheelchair users?"

Grounding with RAG: How AI Systems Retrieve Evidence Before They Answer

Large language models are useful because they can synthesize, explain, and transform information in fluent language. They are unreliable when we ask them to know something current, something private, or something that needs verifiable support. A model may have seen similar material during trainin...

AI-Powered Customer Support — From Chatbot to Intelligent System

Customer support is a useful capstone example because one message can require retrieval, tool use, memory, routing, and approval boundaries at the same time.

Why LLMs Need Help — Hallucinations, Grounding, and the Case for Systems

Large language models produce fluent, confident text. That confidence is the problem. A model can sound authoritative about a property listing that no longer exists, a tax rate that changed last quarter, or a school rating from three years ago. It has no mechanism to check. It was not designed to...