📰 Hackernoon

A practical break-even test for plain RAG, GraphRAG, and hybrid retrieval based on question shape, indexing cost, freshness, latency, permissions, and quality.

A financial RAG system answered a live stock-price query from static filings. Two deterministic refusal checks exposed and blocked the failure.

If your data already has structure that only humans read, the gap between "looks like a graph" and "is a graph your retrieval code uses" is worth checking.

Learn how retrieval-augmented generation (RAG) helps AI SRE agents use runbooks, postmortems, and documentation to investigate real production incidents.

When RAG gives a wrong answer, everyone blames the LLM. Usually the model was fine — retrieval handed it garbage. Here's how to catch it before users do.

RAG systems can confidently generate answers that contradict their own retrieved context, with no errors anywhere to flag it. This article builds a self-healing

RAG is the most important AI pattern in enterprise right now. This complete beginner's guide walks you through building a production-ready RAG.

Most RAG confidence scores only describe the model output. They do not tell you whether the retrieved index was fresh, whether the source changed after indexing

This article argues that the primary security risks in Retrieval-Augmented Generation (RAG) systems often originate in the retrieval layer rather than the langu

A practical framework for auditing enterprise RAG pipelines before legal, security, or compliance teams approve AI agents.

This article argues that evaluating agentic RAG systems requires far more than a single faithfulness score. It explores a production-focused evaluation stack bu

This deep dive argues that chunking is one of the most overlooked determinants of RAG performance. Drawing on recent research from Chroma, Anthropic, Jina AI, a

A naive RAG pipeline pulls 281 docs to deliver 25. Real backpressure pulls 40. How WorkIt paces the producer to consumer demand in Node.js & TypeScript.

Vineet Vijay found 40 K-mismatched vectors silently breaking his RAG system. Here's what he learned, and what he's writing about next.

RAG detectors fail because mean pooling averages out malicious signals in long documents. While a short attack gets diluted, the encoder’s raw hidden states cap

This article examines embedding staleness and index drift as overlooked failure modes in production Retrieval-Augmented Generation systems. Using a real-world R

Standard RAG systems often become hallucination engines because naive PDF parsing destroys document structure. We solved this by implementing layout-aware parti

This article breaks down the five critical decisions required to build effective RAG systems: whether to use retrieval at all, how to chunk and parse data, how

Frustrated by the complexity of existing RAG frameworks, the author built a lightweight, zero-dependency Node.js package that introduces an “agentic” feedback l

Docling Studio is an open-source visual debugger for RAG pipelines built on IBM's Docling. Instead of treating document extraction as a black box, it lets you s

The Threat: Retrieval-Augmented Generation (RAG) pipelines are vulnerable to Indirect Prompt Injection, where malicious instructions hidden in seemingly harmles

Learn how Cohere-embed-multilingual-v3.0 creates embeddings for 100+ languages to power semantic search, retrieval, and recommendation systems.

Learn how pplx-embed-context-v1-0.6b creates context-aware chunk embeddings for RAG systems with int8 efficiency, 32K context, and late chunking.