Retrieval-Augmented Generation vs vector databases
Data-driven comparison powered by the gentic.news knowledge graph
Retrieval-Augmented Generation
technology
vector databases
technology
Ecosystem
Retrieval-Augmented Generation
vector databases
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Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) is a technique that enables large language models (LLMs) to retrieve and incorporate new information from external data sources. With RAG, LLMs first refer to a specified set of documents, then respond to user queries. These documents supplement information from
Recent Events
Retrieval-Augmented Generation
Basic RAG gained prominence as the go-to solution for enhancing LLMs with external knowledge
New study validates retrieval metrics as proxies for RAG information coverage
Gained prominence between 2020 and 2023 but now seen as limited, leading to evolution toward agent memory systems.
New approach achieved 98.7% accuracy on financial benchmarks without vector databases or embeddings
New guide published for building production-ready RAG systems using free, local tools
vector databases
No timeline events