RAG

RAG, or retrieval-augmented generation, is a pattern that grounds a language model’s answer in documents, code, or data fetched at query time rather than relying only on what the model memorized in training. A retriever finds the most relevant passages, and the model generates its answer conditioned on them.

For builders it is the cheapest way to make an LLM answer over private, fresh, or domain-specific knowledge without retraining, and citations make the answers auditable. The quality ceiling is set by retrieval: if the right passage is never fetched, no amount of prompting recovers it.

The common failure is treating RAG as a single trick instead of a pipeline — Chunking, Embeddings, Hybrid Search, Reranking, and RAG Evaluation each move the needle, and a weak link surfaces as a confident but ungrounded answer.

Related: Embeddings Vector Database Hybrid Search Reranking Chunking Context Engineering RAG Evaluation

This is the RAG concept page (definitions and fundamentals). For dated developments, see the RAG Topic hub.

Recent Updates

• 2026-09-07: Leveraging Low-Level Symbolic Competences for Unsupervised Grounding in Hallucination Detection (breakingnewsofficial) · arxiv.org
• 2026-09-07: BIT.UA at BioASQ 14B: Modular Retrieval with PostgreSQL and Qdrant for Biomedical Question Answering (breakingnewsofficial) · arxiv.org — ColBERT
• 2026-09-07: RefactorPlatform: An Open-Source Harness for Controlled Evaluation of Repository-Scale Refactoring Agents (breakingnewsofficial) · arxiv.org — GitHub · OpenRouter
• 2026-09-07: Shadow Queries for Private Retrieval in Vector Databases (breakingnewsofficial) · arxiv.org
• 2026-09-07: CAGE: Coherence-Aware Graph Encoding for Retrieval-Augmented Generation (breakingnewsofficial) · arxiv.org — monoT5
• 2026-09-07: LentEx: Latent Entity Extraction via Synthetic Data and Instruction-Tuned LLMs (breakingnewsofficial) · arxiv.org
• 2026-09-07: Hybrid Search vs. BM25: A 26x Improvement in Grounded Language-Model Retrieval (breakingnewsofficial) · arxiv.org
• 2026-09-07: GRACE: Graph-Grounded Reflective Agent Copilot Engine for Expert-in-the-Loop Knowledge Expansion (breakingnewsofficial) · arxiv.org
• 2026-09-07: VERGE: Verification-Enhanced Refinement for Extracting Cancer Symptoms from Clinical Notes (breakingnewsofficial) · arxiv.org
• 2026-09-07: The Evolution of a Production RAG System: From Basic Grep to Knowledge Graphs and Hybrid Retrieval (breakingnewsofficial) · habr.com — LangChain
• 2026-09-08: Mercury 2.5: Production-Ready Diffusion LLM for Search, Voice, and Coding Agents (breakingnewsofficial) · inceptionlabs.ai — Inception NVIDIA · OpenAI · Google · Anthropic OpenCall Augment Code · Baseten · OpenRouter Shruti Koparkar Oliver Silverstein Mercury 2.5 · Mercury 2 Mercury Voice GPT-5.6 Luna (Low) · Gemini 3.5 Flash-Lite · Claude Haiku 4.5
• 2026-09-09: Memory in Deep Time-Series Models: A Unified Framework for Retention and Access (breakingnewsofficial) · arxiv.org
• 2026-09-09: DI-Bench: Systematic Benchmark Generation for Enterprise Agent Data Intelligence Tasks (breakingnewsofficial) · arxiv.org
• 2026-09-09: Don’t Lose Entities from Retrieval to Generation: Dual Entity Recovery RAG for multi-hop QA (breakingnewsofficial) · arxiv.org
• 2026-09-09: SurveyAgent-HKA: Multi-agent framework for scientific survey generation with LLM and human knowledge (breakingnewsofficial) · arxiv.org
• 2026-09-09: Better Together: Complementary Query Rewriting Under a Strong RAG Baseline (breakingnewsofficial) · arxiv.org — BGE
• 2026-09-10: Personalizing Small Language Models with Individual Text Corpora via DoRA Fine-Tuning (breakingnewsofficial) · arxiv.org
• 2026-09-10: Fine-Tuning a KV Cache Concatenation-Aware Model or Recomputing KV Caches? Why Not Both? (breakingnewsofficial) · arxiv.org
• 2026-09-10: Reproducing Omitted Temporal Expressions in Japanese News for Retrieval-Augmented Applications (breakingnewsofficial) · arxiv.org
• 2026-09-10: HybridDeepResearch Benchmark Reveals AI Agents Struggle With Web and Database Reasoning (breakingnewsofficial) · arxiv.org — Snowflake · OpenAI · Anthropic · Hugging Face · GLM-5.2 · Claude Sonnet 4.6 · GPT-5
• 2026-09-10: Do LLMs Make More Mistakes If They Do Not Believe the Input Data? (breakingnewsofficial) · arxiv.org — Kimi K3
• 2026-09-11: RAG-Safety-Bench: Reliable Evaluation of Retrieval-Augmented LLM Safety (breakingnewsofficial) · arxiv.org
• 2026-09-11: REVA: Reusable Evidence View Aggregation for Context-Efficient RAG Serving (breakingnewsofficial) · arxiv.org
• 2026-09-11: ReGround: Grounding Reviewer Comments in Multimodal Evidence (breakingnewsofficial) · arxiv.org
• 2026-09-11: Think Before You Link: Rarity, Reasoning, and Retrieval in Multilingual Entity Linking (breakingnewsofficial) · arxiv.org

FAQ

What is RAG?

RAG, or retrieval-augmented generation, grounds LLM answers in retrieved documents, code, or data before generation. GROUNDING tracks RAG architecture, retrieval quality, evaluation, grounding failures, and production patterns.

Which concepts are related to RAG?

Related concepts tracked by the radar include Embeddings, Vector Database, Hybrid Search, Reranking, Chunking.

What does this RAG page track?

Dated updates, papers, and mentions of RAG collected by the GROUNDING radar, most recently on 2026-09-11.