RAG — How Retrieval-Augmented Generation Works

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"RAG" stands for Retrieval-Augmented Generation. It's a technique that combines the power of retrieval systems (like search engines) with the generative capabilities of neural networks to enhance the performance of language models.

The RAG approach helps address some of the limitations of standalone LLMs, such as handling queries that require up-to-date information or detailed knowledge not covered in the model's training data. By leveraging external data, RAG models can provide responses that are both informed by a vast amount of information and tailored to the specific context of the query. This method has been particularly useful in question-answering systems, where the accuracy and relevance of the response are paramount.

Here is the video that uses a RAG coding using OpenAI LLM:

Here is the video explaining Chat with RTX, which is another RAG system using a local LLM:

Thank you!

Dr. Shahriar Hossain

#AI #aimodel #aimodels #rag #llm #aitutorial