In the AI era, defining problems is often more important than solving them; here we explore AI's contradictions, boundaries, and possibilities.
The article examines RAG vector retrieval filtering strategies, comparing pre‑filtering (filter before vector search) and post‑filtering (filter after ANN search), and introduces single‑stage filtering, discussing their principles, trade‑offs, suitable scenarios, and architectural implications for accuracy and performance.
The article explains why a structured test set is essential for Retrieval‑Augmented Generation systems, outlines failure modes, describes layered evaluation of retrieval and generation, details infrastructure like chunk IDs and manifests, and provides a complete annotation pipeline with cold‑start and adversarial strategies.
This guide explains how Claude Code can be deployed in massive monorepos, legacy systems, and distributed repositories, detailing navigation methods, the limits of RAG, the benefits of agentic search, and a five‑layer support system—including CLAUDE.md, hooks, skills, plugins, and MCP servers—to help teams of thousands achieve reliable AI‑assisted coding.
When a long document is split into many highly similar chunks, vector‑based top‑k retrieval tends to return multiple pieces from the same source, causing document dominance; applying a per‑document chunk limit together with Maximal Marginal Relevance (MMR) re‑ranking introduces diversity while preserving relevance, offering a low‑cost way to improve RAG answer quality.
The article defines AI‑native as a system whose existence depends on AI at every layer, contrasts it with AI‑enabled and AI‑first, explains the structural layers, role shifts, bottlenecks, and maturity stages, and offers concrete guidelines for building truly AI‑native engineering practices.
The article outlines a four‑stage agent development lifecycle—Build, Test, Deploy, Monitor—explaining how early, iterative delivery, systematic testing, controlled deployment, and continuous monitoring transform experimental agents into reliable production systems while addressing governance, cost, and scalability challenges.
The article catalogs dozens of AI agent failure modes—from one‑shot attempts and cold‑start amnesia to hidden harness control—and explains why these issues quickly overwhelm developers, then outlines concrete mitigation strategies and their trade‑offs.
The article examines the emerging A2A (Agent‑to‑Agent) protocol, tracing its evolution from function calling to MCP and finally A2A, and evaluates its core concepts, security model, task lifecycle, transport options, design guidelines, and operational best practices for building interoperable AI agent systems.
The article examines Model Context Protocol (MCP), introduced by Anthropic in November 2024, tracing its rapid adoption, architectural design—including Host/Client/Server roles, transport layers, security and observability practices—and outlines production guidelines, future roadmap, and current limitations.
The article analyzes how large language models process only tokenized text, compares the traditional LLM‑plus‑toolchain pipeline with emerging multimodal models, evaluates their cost, speed, controllability, and hallucination risks, and proposes a hybrid architecture that matches each approach to specific document scenarios.
