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Large Language Models (LLMs) often feel like they remember past conversations or facts, but under the hood their memory works very differently from human memory. This article explores how LLMs handle context, why it seems like they remember things, and how engineers simulate memory for them.
If you've ever tried building an AI agent using modern orchestration frameworks, you might have felt a strange sense of déjà vu — like you're back in the early 2000s dealing with DLL Hell. Back then, software broke because a tiny shared library had the wrong version or wasn't registered correctly.
Building AI infrastructure today feels like chasing a moving target. By the time most teams finish implementing something labeled cutting-edge, the technology landscape has already moved ahead. Months spent carefully crafting LangChain workflows or MCP connectors often become outdated before they're even fully deployed.
Picture a group of highly skilled professionals in one office. Each person is an expert in a particular domain — finance, marketing, customer service, logistics — yet there's no single manager telling them what to do. Instead, they each listen for signals relevant to their specialty (for example, a new sale happened or inventory is low) and take action immediately. …
Imagine you're about to flip a coin. Before you do, you might wonder: What's the chance it lands on heads? Or consider picking a card from a standard deck: What's the probability of drawing an Ace? These everyday questions tap into the essence of Probability Theory — a mathematical framework for quantifying uncertainty and making informed predictions.
Whether you're a technology enthusiast eager to explore new advancements, a visionary business leader looking to leverage cutting-edge tools, or simply someone curious about the inner workings of artificial intelligence, this guide is designed with you in mind.