Retrieval-augmented generation (RAG) has become the de facto standard for grounding large language models (LLMs) in private data. The standard architecture — chunking documents, embedding them into a ...
Through natural language queries and graph-based RAG, TigerGraph CoPilot addresses the complex challenges of data analysis and the serious shortcomings of LLMs for business applications. Data has the ...
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Beyond RAG as a buzzword: Aligning AI retrieval with real-world use
RAG talk is prevalent, yet simply stating “we used RAG” fails to provide much information about its effectiveness in a production environment. A more effective approach is to examine how you align ...
Generative AI depends on data to build responses to user queries. Training large language models (LLMs) uses huge volumes of data—for example, OpenAI’s GPT-3 used the CommonCrawl data set, which stood ...
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