Retrieval-augmented generation (RAG) is an AI framework designed to augment an LLM by integrating it with information retrieved from an external knowledge base. Based on the increasing focus RAG has garnered lately, it is reasonable to conclude that RAG is now a prominent topic in the AI/NLP (Artificial Intelligence/Natural Language Processing) ecosystem. Therefore, let’s jump in and discuss what to expect from RAG systems when paired with self-hosted LLMs.

In the blog post titled: “Discover the Performance Gain with Retrieval Augmented Generation,” we investigated how the number of retrieved documents can improve the quality of LLM answers. We also described how the vectorized LLM based on the MMLU dataset, stored in a vector database such as MyScale, generates more accurate responses when integrated with contextually relevant knowledge and without fine-tuning the dataset.

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