Long Context RAG Performance of LLMs | Databricks Blog
Retrieval Augmented Generation (RAG) is the most widely adopted generative AI use case among our customers. RAG enhances the accuracy of LLMs by retrieving information from external sources such as unstructured documents or structured data. With the availability of LLMs with longer context lengths like Anthropic Claude (200k context length), GPT-4-turbo (128k context length) and Google Gemini 1.5 pro (2 million context length), LLM app developers are able to feed more documents into their RAG applications. Taking longer context lengths to the extreme, there is even a debate about whether long context language models will eventually subsume RAG workflows. Why retrieve individual documents from a database if you can insert the entire corpus into the context window? This blog post explores the impact of increased context length on the quality of RAG applications. We ran over 2,000 experiments on 13 popular open source and commercial LLMs to uncover their performance on various domain-s
Long Context RAG Performance of LLMs | Databricks Blog Skip to main content Retrieval Augmented Generation (RAG) is the most widely adopted generative AI use case among our customers. RAG enhances the accuracy of LLMs by retrieving information from external sources such as unstructured documents or structured data. With the availability of LLMs with longer context lengths like Anthropic Claude ( 200k context length ), GPT-4-turbo ( 128k context length ) and Google Gemini 1.5 pro ( 2 million context length ), LLM app developers are able to feed more documents into their RAG applications. Taking
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