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Interesting article to improve RAG: https://t.co/K26jbs7OvM Repeat a few times: Chunk and embed fragments of text Group similar embeddings summarize This leads to better retrieval-augmented models
Boosting the Capabilities of Compact Models in Low-Data Contexts with Large Language Models and Retrieval-Augmented Generation Presents a RAG framework combining small models with LLMs to improve morphological glossing for low-resource languages. 📝https://t.co/d3MCeA4Gkd https://t.co/TNMsfT88FP
Contextual retrieval is a very simple technique (generate a summary of the chunk by grounding it in the context of the entire document), but it's only really cost effective with prompt caching. I'm excited about prompt caching (Anthropic, Gemini, and the new OpenAI DevDay… https://t.co/pKwqBhFfey https://t.co/dQWewH6nwb
AnthropicAI has introduced Contextual Retrieval, a significant advancement in Retrieval-Augmented Generation (RAG) that enhances text segmentation by adding contextual information to chunks. This technique, which involves prepending metadata to each chunk to detail its position within the document, is made cost-effective through prompt caching. The approach has shown improvements in various data evaluations, including code, fiction, and arXiv. Additionally, the use of bm25 and semantic retrieval remains standard in RAG. Collaborations such as the one between VAST Data and NVIDIA, leveraging tools like VAST InsightEngine, are contributing to the mainstream adoption of generative AI by leveraging these advancements in RAG.
