rag

An educational article from MachineLearningMastery covering seven chunking strategies for RAG pipelines, from fixed-size token chunking to advanced semantic and hierarchical approaches, with guidance on when to use each.

This article argues that LLM hallucinations in production are typically a system architecture problem rather than a model problem, and outlines four key guardrails: RAG grounding, live tools/function calling, selective human oversight, and red teaming/adversarial testing.

This paper presents a triple-robustness analysis of RAG for multi-hop traceability, varying embedder, corpus, and judge across thousands of runs. It finds that over-citation is architecturally universal in GraphRAG but its faithfulness consequences are corpus-conditional, and LLM-as-judge faithfulness is fragile across retrieval states.

This paper presents a benchmark and trace-logging framework for evaluating LLM-based agents that control microscopes, comparing 105 agent configurations and finding that benchmarks support qualification but do not reliably predict performance on unseen tasks.

Arkon is a self-hosted open-source Enterprise AI Knowledge Hub that acts as an MCP server, compiling internal docs into a structured wiki and serving permission-scoped context to Claude and other LLMs.

Introduces CROWN-QA, a benchmark for completeness-sensitive negative reasoning in LLMs, showing models struggle to distinguish justified negative answers from insufficient evidence, often over-closing.

This paper proposes a framework to elicit intrinsic hallucinations in LLMs using semantically equivalent adversarial perturbations, showing that state-of-the-art models degrade significantly in contextual faithfulness even with meaning-preserving query variations.

This paper introduces RAG-Stack, a framework that co-optimizes RAG serving performance and answer quality by efficiently exploring the joint algorithm-system configuration space. It finds Pareto frontiers that cover significantly more quality-performance space than existing configuration-search methods.

NotebookLlaMa is a fully open-source alternative to NotebookLM, built on LlamaCloud with an MCP server and Streamlit interface for turning source material into a research workspace.

It explains the evolution path of RAG technology from basic RAG in 2020 to autonomous agents + reinforcement learning in 2025-2026, covering retrieval precision improvement, reflection and correction, graph enhancement, routing optimization, and Agentic RAG, and summarizes representative works and pain points of each stage.

Presents an evidence-grounded multimodal pipeline that constructs a provenance-rich knowledge graph from lecture videos, combining ASR, OCR, and vision-language models, achieving high retrieval accuracy on neural network lectures.

A practitioner argues that RAG is no longer the automatic solution for enterprise AI, pointing out that many problems are really about data hygiene or structured queries, and that agents with tool use are often better.

This post explains how integrating MCP (Model Context Protocol) into Agentic RAG systems allows each data domain to manage its own MCP servers, enabling standardized data access, security, and decoupled evolution of retrieval systems.

Amit Shekhar published a comprehensive open-source repository of AI engineering interview questions and answers, covering LLM fundamentals, prompt engineering, RAG, AI agents, fine-tuning, vector databases, LLMOps, and more.

This paper introduces SANE, a lightweight plugin for retrieval-augmented generation that improves both retrieval and reading by selecting top candidates via synopses and performing blueprint-guided query-time evidence extraction.

This paper shows that per-chunk verification fails for multi-hop RAG because no single chunk is sufficient, and proposes decomposition-based verification to repair it, demonstrating significant improvements across multiple datasets.

This arXiv paper presents a unified LLMOps architecture for real-time, enterprise-ready LLM deployments, integrating data ingestion, continual learning, RAG, and feedback loops. It introduces components like AIPO, STAR+FAR, and SAGE to address knowledge staleness, hallucination, and latency-cost trade-offs in regulated sectors.

RAG-Anything is a multimodal document-processing RAG system built on LightRAG that parses documents, constructs a multimodal knowledge graph, and uses hybrid vector-graph retrieval to answer queries.

The author benchmarks classic vector RAG against Google's new Open Knowledge Format (OKF) and a hybrid approach using a local stack (Ollama, ChromaDB), finding hybrid retrieval answers more questions correctly but at higher token cost, while highlighting specific failure modes.

This paper presents a production extraction layer that converts heterogeneous documents into an ontology-aligned knowledge graph using a locally hosted tuned Qwen LLM, with ontology-guided prompts, multi-stage deduplication, and embedding-based resolution. Evaluation on intelligence corpora improved search recall from about 70 to 95 percent with no false merges.