Retrieval & Knowledge

當 RAG 不夠用時：快取增強生成（CAG）、混合檢索與工作記憶

基本的檢索增強生成（RAG）到現在還是多數正式環境系統最常用的基礎模式。語料庫很大、更新頻繁、又需要展示答案來源？檢索依然是最乾淨的起點。不過實務上會碰到一種極限情境——簡單的 RAG 開始力不從心：系統確實找到了正確的文件，但任務現在需要的是針對一組有限的證據持續推理，同時回答同一案件的多次後續追問。

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。

When RAG Is Not Enough: CAG, Hybrid Retrieval, and Working Memory

Basic retrieval-augmented generation, or RAG, is still the default grounding pattern for most production systems. If you have a large corpus, frequent updates, and a need to show where an answer came from, retrieval remains the cleanest starting point. But there is a practical limit case where si...

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...