Retrieval-Augmented Generation (RAG) is a powerful technique that combines the strengths of retrieval-based systems and generative AI, particularly large language models (LLMs). While LLMs like GPT excel at generating human-like text, they can struggle with fact-based or real-time information. RAG bridges this gap by allowing the model to retrieve relevant external data during the generation process.
RAG works by first retrieving documents or data from an external knowledge base, such as databases, websites, or enterprise systems, based on the user’s query. The retrieved information is then fed into the generative model, allowing it to create more accurate, context-aware responses.
This approach is useful in applications such as customer support, where up-to-date, factual information is critical, or in research settings where LLMs can generate insights while pulling in relevant references. RAG enhances the reliability and scope of large language models, making them more effective in real-world scenarios.
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