Retrieval-Augmented Generation (RAG) is an AI framework that enhances the performance of large language models (LLMs) by integrating external knowledge sources into their response generation process. Unlike traditional LLMs that rely solely on static training data, RAG retrieves relevant information from databases, documents, or web sources before generating a response. This approach improves accuracy, relevance, and domain specificity while reducing hallucinations and retraining costs.
RAG operates in two phases:
- Retrieval: Relevant information is fetched from external sources based on the user query.
- Generation: The retrieved data is combined with the LLM’s internal knowledge to create a tailored response.
Key benefits include:
- Providing up-to-date and domain-specific answers.
- Reducing computational costs by avoiding frequent model retraining.
- Enhancing transparency by including source references in responses.
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