Like i am thinking rightnow that if LLM are trained with very large datasets and proper ML and other alogorithms , then why to involve RAG systems in the whole process to retrieve information?
Even though LLMs are trained on massive datasets, their knowledge is static and compressed into model parameters, which means they can become outdated, miss fine-grained details, or hallucinate facts. Updating them requires costly retraining, and they’re not ideal for handling frequently changing or highly specific data.
RAG systems complement LLMs by enabling real-time retrieval of relevant information from external sources. This improves accuracy, allows access to private or domain-specific data without retraining, and adds transparency by grounding responses in actual documents. In practice, LLMs handle reasoning and generation, while RAG ensures the information is current and reliable.
I hope this answer your question
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indeed thankyou
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