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Showing posts from September, 2026

AI Memory Explained: How AI Systems Store & Retrieve Information

  AI Memory Explained: How AI Systems Store and Retrieve Information Introduction Ask an AI chatbot a question today, and it might respond thoughtfully and accurately. Ask it the same question tomorrow, in a brand-new conversation, and by default it has no idea you ever spoke before — no memory of your preferences, your past questions, or anything you told it yesterday. This is one of the more counterintuitive aspects of how large language models actually work: despite feeling conversational and personable, a model has no built-in, persistent memory of its own. Every one of its abilities to "remember" something across turns or across sessions is the result of deliberate engineering built around the model, not a native capability of the model itself. This article explains how AI memory actually works — what "memory" really means for a system built on top of a language model, the different layers of memory that real systems implement, how information actually gets ...
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AI Memory Explained: How AI Systems Store & Retrieve Information

  AI Memory Explained: How AI Systems Store and Retrieve Information Introduction Ask an AI chatbot a question today, and it might respond thoughtfully and accurately. Ask it the same question tomorrow, in a brand-new conversation, and by default it has no idea you ever spoke before — no memory of your preferences, your past questions, or anything you told it yesterday. This is one of the more counterintuitive aspects of how large language models actually work: despite feeling conversational and personable, a model has no built-in, persistent memory of its own. Every one of its abilities to "remember" something across turns or across sessions is the result of deliberate engineering built around the model, not a native capability of the model itself. This article explains how AI memory actually works — what "memory" really means for a system built on top of a language model, the different layers of memory that real systems implement, how information actually gets ...
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Fine-Tuning vs RAG: How to Customize an AI Model (Full Guide)

  Fine-Tuning vs RAG: How to Customize an AI Model Introduction Once a team decides to build a product around a large language model, one question comes up almost immediately: the general-purpose model is impressive, but it doesn't know our data, doesn't speak in our brand's voice, and doesn't follow our specific workflows — how do we actually customize it? Two techniques dominate the conversation: fine-tuning, which further trains the model's own internal parameters on a custom dataset, and Retrieval-Augmented Generation (RAG), which leaves the model's parameters untouched but feeds it relevant external information at the moment of answering a question. These two approaches are frequently framed as competitors, and teams often ask which one they should choose. That framing, while understandable, is usually the wrong way to think about the decision. Fine-tuning and RAG solve genuinely different problems, and the right question isn't "which is better...
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