In the rapidly evolving world of AI, large language models have come a long way, boasting impressive knowledge of the world around us. Yet LLMs, as intelligent as they are, often struggle to recognize the boundaries of their own knowledge, a shortfall that often leads them to “hallucinate” to fill in the gaps. A newly devised technique, known as Retrieval Augmented Generation (RAG), shows promise in efficiently increasing the knowledge of these LLMs and reducing the impact of hallucination by enabling prompts to be augmented with proprietary data.
Navigating the Knowledge Gap in LLMs
LLMs are computer models capable of comprehending and generating human-like text. They're the AI behind your digital assistant, autocorrect function, and even some of your emails. Their knowledge of the world is often immense, but it isn't perfect. Just like humans, LLMs can reach the limits of their knowledge, but instead of stopping, they tend to make educated guesses or “hallucinate” to complete the task. This can lead to results that contain inaccurate or misleading information.