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.

This article systematically explains context-compression methods for production AI agents, distinguishing message trimming, tool-result cleanup, artifact externalization, structured summaries, and native compaction. Using Hermes Agent, LangChain v1, and LangGraph v1, it presents a layered implementation and evaluation approach.

An intuitive introduction for programmers without a machine-learning background to gradient descent, loss functions, backpropagation, learning rates, and optimizers in LLM training, from next-token prediction through parameter updates.

Using a simplified online bookstore as one continuous example, this guide explains the software diagrams commonly used for requirements, business, architecture, data, interaction, and deployment design.

Starting with asyncio.run(), coroutines, Tasks, and Futures, learn how Python’s event loop schedules waits, timers, and I/O, and where sequential await, concurrent tasks, and blocking code differ.

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.