RAG is a groundbreaking approach that combines the strengths of information retrieval (IR) techniques with the creative capabilities of LLMs. This improvement transforms LLMs from being merely conversationalists to experts capable of engaging in in-depth and contextually rich dialogues on specialized topics, significantly enhancing their use and applicability across various domains.
Large Language Models (LLMs) are typically trained to converse on a wide range of topics with relative ease. However, their responses often lack depth and specificity and they might struggle to engage in detailed discussions on specialized subjects due to a lack of domain-specific knowledge. To overcome this, RAG fetches relevant information from different data sources in real-time and incorporates it into its responses; With it, the RAG model acts as an expert that evolves a general LLM into a specialized one, capable of retrieving and utilizing relevant information to provide precise responses, even to queries that require knowledge beyond its initial training data.
