What are the limitations of LLMs that RAG aims to address?

The limitations of LLMs that RAG aims to address include hallucinations, outdated data, and lack of access to private data. RAG enhances LLM responses by providing accurate context and information from external sources.

RAG addresses several key limitations of Large Language Models (LLMs). First, it mitigates hallucinations, where models generate probabilistic answers that may not be valid. Second, it tackles the issue of outdated data, as LLMs are often trained on static datasets and may not reflect current information. Lastly, RAG allows models to access private data, which they typically cannot do, enabling them to provide more relevant and specific responses based on user queries.