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LLMs do not forget because they are broken. They forget because memory is hard. Long context windows sound impressive, but real intelligence is not about storing everything forever. It is about compression, relevance, hierarchy, and timing. This article breaks down why long context is not long-term understanding, why forgetting is often a feature rather than a bug, and how true AI systems will evolve toward structured memory and reflection.

Retrieval-Augmented Generation (RAG) was never meant to stop at “retrieve and prepend.” Classic RAG helped LLMs access external knowledge, but real-world problems forced it to evolve. This article explains, in simple but technical language, how RAG is moving from basic retrieval into Modular RAG, Agentic RAG, and Graph-Enhanced RAG ,transforming RAG from a lookup tool into an adaptive external memory and reasoning engine.

Modern AI models sometimes act like “Yes-Men,” agreeing with false statements instead of correcting them. This happens because Large Language Models aren’t truth engines , they’re pattern completers. This article explains in simple language how model design creates sycophancy, and why **Context Engineering** is the key to fixing it.