Evaluation

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 programmer-oriented guide to Multi-Agent and Sub-Agent system design: when multiple Agents are needed, task decomposition and coordination patterns, context, permissions, reliability, evaluation, and production delivery.

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.

Introduces OpenTelemetry’s core components and observability signals, and explains how to use them with AI agents, RAG, and production systems.

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.