@chase_ai_: Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of large language models (LLMs) by giving them access to external, up-to-date information. Without RAG, an LLM's knowledge is limited to the data it was trained on, which can lead to it generating outdated, inaccurate, or "hallucinated" responses. RAG addresses this by first retrieving relevant information from a designated knowledge base, such as a set of documents, a database, or even the internet. This retrieved information is then fed into the LLM as context, allowing the model to generate a more accurate and informed response. In essence, RAG acts as a search and synthesis tool, enabling the LLM to provide grounded, factual answers beyond its original training data.

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Saturday 13 September 2025 22:30:45 GMT