Rag
7 articles
Choosing the Right Fix for LLM Hallucinations: A Lifecycle-Based Decision Framework
Choosing the right strategy to mitigate LLM hallucinations requires diagnosing the error type (intrinsic vs. extrinsic) and considering the intervention stage in the LLM lifecycle. This article presents a decision framework mapping core strategies like RAG, fine-tuning, and prompt engineering to specific hallucination types and implementation complexity.
Milvus vs. Weaviate vs. Qdrant: A Decision Framework for Open-Source Vector Databases
This article presents a decision framework for selecting among open-source vector databases Milvus, Weaviate, and Qdrant for RAG and semantic search applications. It analyzes their distinct strengths in scalability, features, and operational simplicity to guide your choice.
How Knowledge Graphs Make AI Answers More Accurate
This article explains how integrating knowledge graphs into Retrieval-Augmented Generation (RAG) systems significantly enhances AI's factual accuracy and reasoning capabilities. By moving beyond unstructured text, knowledge graphs provide a structured foundation that reduces AI hallucinations and improves reliability.
What Are Vector Databases and How Do They Power AI Applications?
Retrieval-Augmented Generation (RAG) is the primary use case driving vector database adoption in 2026.
New RAG Methods Bypass Traditional OCR for Searchable PDFs
A new benchmark reveals traditional optical character recognition (OCR) frequently fails across 11 challenging document types, directly impacting AI-powered Retrieval-Augmented Generation (RAG) system
Vision LLMs unlock chart and diagram understanding for RAG systems
In a recent benchmark, Vision LLMs correctly answered complex questions about historical voter turnout from a multi-decade Indian election chart, a task impossible for traditional text-based AI, accor
Proxy-Pointer RAG builds accurate, private knowledge graphs
A new AI framework builds highly accurate knowledge graphs from sensitive data, such as exercise records, without compromising privacy.