RAG

A concise RAG evaluation reference organized by retrieval, context, answer, citation, refusal, security, and production metrics, with practical formulas and a minimal release-gate set.

A RAG reranking design guide for AI Application Engineers. It distinguishes retrieval fusion, semantic reranking, business ranking, and evidence-set selection, then provides a production-ready default pipeline.

Based on 20 public job descriptions each from mainland China, the United States, and Tokyo, this study maps the hiring stack for AI Application, Agent, and RAG engineers and gives a Tokyo-focused learning and portfolio roadmap.

A systematic guide to lexical retrieval, dense retrieval, hybrid fusion, reranking, evaluation, observability, and production governance in RAG systems.

Explains how to build an observability and evaluation system for RAG that covers runtime execution, retrieval quality, generation quality, and continuous regression testing.

An introduction to RAG chunking design, progressing from fixed chunks to structural, contextualized, and evaluation-driven approaches.

Explains the role, calculation, appropriate use cases, and main limitations of RRF in multi-retriever fusion for RAG.

An introduction to the roles of dense search, sparse search, RRF, and rerankers in an enterprise RAG retrieval pipeline.

Use this article as a reference when building an enterprise RAG system.

Starting from the trend of platforms absorbing basic RAG pipelines, this article examines the high-premium skills AI application engineers should prioritize in 2026: evaluation and observability, data governance and access control, and deep engineering capabilities for agentic workflows.