Retrieval-Augmented Generation vs Agentic RAG
Data-driven comparison powered by the gentic.news knowledge graph
Retrieval-Augmented Generation
technology
Agentic RAG
technology
Ecosystem
Retrieval-Augmented Generation
Agentic RAG
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
Agentic RAG
In the context of generative artificial intelligence, AI agents are a class of intelligent agents distinguished by their ability to operate autonomously in complex environments. Agentic AI tools prioritize decision-making over content creation and do not require continuous oversight.
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
Agentic RAG
Researchers propose test-time modifications to agentic RAG systems with contextualization and de-duplication modules
Emergence of agentic RAG systems that introduce decision-making capabilities at the retrieval stage
Research paper analyzes shift from tools to agentic AI systems for human-AI teaming