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Large Language Models (LLMs) have transformed artificial intelligence into something tangible. They can write essays, debug code, generate poetry, and even reason across domains. Yet, for all their power, they share a surprisingly human flaw they forget everything between conversations.
When we close a chat, the model’s knowledge vanishes. Ask it about an event after its training cutoff, and it fabricates with confidence. These models are not lying; they simply don’t know. They operate like well-read but amnesic scholars, brilliant in syntax, limited in substance.
The fundamental limitation lies in how they are…
6 min readJust now
–
Press enter or click to view image in full size
Large Language Models (LLMs) have transformed artificial intelligence into something tangible. They can write essays, debug code, generate poetry, and even reason across domains. Yet, for all their power, they share a surprisingly human flaw they forget everything between conversations.
When we close a chat, the model’s knowledge vanishes. Ask it about an event after its training cutoff, and it fabricates with confidence. These models are not lying; they simply don’t know. They operate like well-read but amnesic scholars, brilliant in syntax, limited in substance.
The fundamental limitation lies in how they are trained. An LLM captures statistical regularities of language, not dynamic facts about the world. It has seen the world’s library but cannot walk back into it.
This problem, the inability of models to access or update their knowledge post-training, gave rise to one of the most influential paradigms in modern AI engineering: Retrieval-Augmented Generation, or RAG.
What Does “Memoryless” Really Mean?
To understand why RAG matters, we first need to dissect what it means for a model to be memoryless.
When we train a transformer, it learns correlations between tokens over massive…