Technology ❯ Artificial Intelligence ❯ Natural Language Processing ❯ Large Language Models

Retrieval-Augmented Generation

New Framework Sets Trust Standards for Retrieval-Augmented AI With Benchmark Exposing Gaps

An arXiv survey urges targeted fixes across six trust areas to make retrieval-grounded chatbots safer.

RAG Research Moves Beyond Pure Vectors With New Methods for Structure, Chunking, and Verification

RAG Shifts From Vector Similarity to Reasoning-Driven Retrieval

RAG Research Delivers New Benchmark and Clear Guidance on Retrieval, Reasoning, and Bias

RAG Enters Production With Data Engineering Playbooks and a Practical PDF Chat Build

New RAG Research Debuts Adaptive Retrieval Advances as Security Risks Emerge

Two New RAG Preprints Propose Theme-Aligned Hypergraphs and Hierarchical Agents

RAG Advances Spotlight Structured Retrieval Gains and Safety Risks in Agentic RL Search

RAG Research Enters Next Phase With Hybrid Retrieval, Dynamic Graphs, and Context Valuation

Adversarial Prompt Attack Puts RAG on Alert as Practitioners Tighten Defenses

New RAG Preprints Propose Retrieval, Reasoning, and Graph Methods to Curb Hallucinations

New Studies Advance Reliability‑Aware, Graph‑Based and Monitored RAG as Real‑World Test Flags Failures

September Research Blitz Reveals LLM Vulnerabilities and Rolls Out Real-World Benchmarks

RAG Matures for Enterprise Use With On‑Prem Pipelines and Smarter Embedding Strategies

Developers Embrace RAG to Ground Language Models in Accurate, Up-to-Date Data