Retrieval-Augmented Generation (RAG) is a method used to make Large Language Models less prone to hallucinating by grounding their output in retrieved data.

Authors

• Person: Not available Organization: PMC
  A self-correcting Agentic Graph RAG for clinical decision support in ...
• Person: Sewak, Ph.D. Organization: LinkedIn
  Hallucinations in LLMs: Can You Even Measure the Problem?
• Person: Not available Organization: Amazon Web Services
  Detect hallucinations for RAG-based systems - AWS
• Person: Not available Organization: Academic Journal of Science and Technology
  Knowledge Graph Combined with Retrieval-Augmented Generation ...
• Person: Not available Organization: Springer
  A survey on augmenting knowledge graphs (KGs) with large ...
• Person: Not available Organization: arXiv
  Efficient Knowledge Graph Construction and Retrieval from ... - arXiv
• Person: Not available Organization: LinkedIn
  Knowledge Graphs Enhance LLMs for Contextual Intelligence

Sources

• A self-correcting Agentic Graph RAG for clinical decision support in ... pmc.ncbi.nlm.nih.gov PMC via serper
• Hallucinations in LLMs: Can You Even Measure the Problem? www.linkedin.com Sewak, Ph.D. · LinkedIn via serper
• Detect hallucinations for RAG-based systems - AWS aws.amazon.com Amazon Web Services via serper
• Knowledge Graph Combined with Retrieval-Augmented Generation ... drpress.org Academic Journal of Science and Technology via serper
• A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com Springer via serper
• Efficient Knowledge Graph Construction and Retrieval from ... - arXiv arxiv.org arXiv via serper
• Knowledge Graphs Enhance LLMs for Contextual Intelligence www.linkedin.com LinkedIn via serper

Referenced by nodes (3)

• Large Language Models concept
• hallucination concept
• Retrieval-Augmented Generation (RAG) concept