https://sinimite.work/en/categories/aiapplicationengineering/Recent content in AIApplicationEngineering on SiBloghttps://sinimite.work/images/og-default.svg?v=20260525-210321https://sinimite.work/images/og-default.svg?v=20260525-210321Hugo -- 0.156.0en-USFri, 17 Jul 2026 15:40:44 +0900https://sinimite.work/en/posts/rag-retrieval-engineering-guide/Fri, 17 Jul 2026 15:40:44 +0900https://sinimite.work/en/posts/rag-retrieval-engineering-guide/A technical report on designing and implementing the RAG retrieval layer, covering BM25, embeddings, ANN, RRF, reranking, query understanding, access control, evaluation, and deployment practices.https://sinimite.work/en/posts/rag-observability-evaluation-design/Fri, 17 Jul 2026 15:06:18 +0900https://sinimite.work/en/posts/rag-observability-evaluation-design/A systematic guide to RAG observability and evaluation, including staged traces, runtime metrics, evaluation datasets, quality scoring, experiment comparison, privacy boundaries, and graceful degradation.https://sinimite.work/en/posts/opentelemetry-ai-agent-observability-guide/Thu, 16 Jul 2026 21:08:52 +0900https://sinimite.work/en/posts/opentelemetry-ai-agent-observability-guide/A beginner's guide to OpenTelemetry for AI application developers, covering traces, metrics, logs, profiles, context propagation, the Collector, GenAI semantic conventions, and production practices.https://sinimite.work/en/posts/rag-chunking-strategies-evaluation/Wed, 15 Jul 2026 18:05:16 +0900https://sinimite.work/en/posts/rag-chunking-strategies-evaluation/A technical reference covering RAG chunking strategies, parameter selection, context preservation, and evaluation workflows.https://sinimite.work/en/posts/rag-rrf-retrieval-fusion/Wed, 15 Jul 2026 17:37:44 +0900https://sinimite.work/en/posts/rag-rrf-retrieval-fusion/A technical reference on the principles of Reciprocal Rank Fusion, its place in the retrieval pipeline, fusion choices, and production evaluation.https://sinimite.work/en/posts/rag-dense-sparse-hybrid-search/Wed, 15 Jul 2026 17:25:30 +0900https://sinimite.work/en/posts/rag-dense-sparse-hybrid-search/A technical reference on Dense + Sparse Hybrid Search, including its principles, fusion methods, suitable use cases, and evaluation approach.https://sinimite.work/en/posts/enterprise-rag-system-building-guide/Wed, 15 Jul 2026 16:35:23 +0900https://sinimite.work/en/posts/enterprise-rag-system-building-guide/An introduction to the architecture of enterprise RAG data ingestion, hybrid retrieval, access control, generation, evaluation, and observability.https://sinimite.work/en/posts/points-of-the-podcast-language-agents-from-reasoning-to-acting/Sun, 24 May 2026 16:05:10 +0900https://sinimite.work/en/posts/points-of-the-podcast-language-agents-from-reasoning-to-acting/Reflections on the podcast Language Agents: From Reasoning to Acting. Starting from ReAct, Reflexion, Tree of Thoughts, memory, benchmarks, and ACI, the article explains why an LLM Agent is an engineered system composed of a model, tools, memory, environment, evaluation, and UX.https://sinimite.work/en/posts/ai-engineer-skill-value-map-2026/Sun, 03 May 2026 20:09:15 +0900https://sinimite.work/en/posts/ai-engineer-skill-value-map-2026/A skill value map for engineers moving into AI application engineering, explaining how the value of basic RAG, prompt engineering, and framework APIs is changing, and why evaluation, governance, and agentic workflows deserve greater investment.https://sinimite.work/en/posts/ai-agent-architecture-design-guide/Sat, 02 May 2026 00:00:00 +0000https://sinimite.work/en/posts/ai-agent-architecture-design-guide/A practical guide to AI coding agents explaining how specs, fat skills, a thin harness, deterministic tooling, and a learning loop create reliable, continuously improving AI agent systems.https://sinimite.work/en/posts/ai-coding-agent-software-spec-best-practices/Thu, 09 Apr 2026 20:03:59 +0900https://sinimite.work/en/posts/ai-coding-agent-software-spec-best-practices/A guide to writing software specifications in the age of AI coding agents, covering the definition of a Software Spec, its six core areas, a four-stage workflow, best practices, and a reusable template.https://sinimite.work/en/posts/agent-native-documentation-engineering/Sun, 29 Mar 2026 16:55:53 +0900https://sinimite.work/en/posts/agent-native-documentation-engineering/A guide to documentation architecture for AI coding agent projects, covering the layered responsibilities of AGENTS.md, PRD, Architecture, Spec, and Plan documents, plus the organizational principles and engineering value of agent-native documentation.https://sinimite.work/en/posts/ai-coding-agent-documentation-best-practices/Sat, 28 Mar 2026 00:00:00 +0000https://sinimite.work/en/posts/ai-coding-agent-documentation-best-practices/Drawing on OpenAI's Harness Engineering, Anthropic's research on long-running agents, the AGENTS.md standard, and experience from multiple production teams, this article presents best practices for designing project documentation for AI coding agents.https://sinimite.work/en/posts/llm-agentic-evolution/Sat, 28 Mar 2026 00:00:00 +0000https://sinimite.work/en/posts/llm-agentic-evolution/LLMs are evolving from passive question-answering tools into agents that pursue goals autonomously. This is not a linear increase in model capability but a fundamental change in the usage paradigm. This article examines the nature of that transition, the evolution of the engineering stack, and what it means for engineers.https://sinimite.work/en/posts/ai-agent-standards-full-landscape/Sat, 28 Mar 2026 00:00:00 +0000https://sinimite.work/en/posts/ai-agent-standards-full-landscape/At the beginning of 2026, standardization in the AI Agent field is advancing at astonishing speed. Four core standards—MCP, A2A, AGENTS.md, and SKILL.md—have each found their place, and AAIF has begun operating as a governance organization. Using analogies to the Java ecosystem, this article helps backend engineers develop a panoramic view of the standards landscape.https://sinimite.work/en/posts/rag-system-complete-guide/Fri, 27 Mar 2026 15:06:20 +0900https://sinimite.work/en/posts/rag-system-complete-guide/A panoramic RAG guide covering ingestion, chunking, retrieval, reranking, generation, evaluation, and production operations, with an implementation path from zero to launch.https://sinimite.work/en/posts/spec-driven-development-guide/Thu, 19 Mar 2026 00:01:55 +0900https://sinimite.work/en/posts/spec-driven-development-guide/A systematic guide to the definition, workflow, tooling ecosystem, and appropriate boundaries of Spec-Driven Development, explaining why it is replacing Prompt-and-Pray AI development.https://sinimite.work/en/posts/rag-chunking-best-practices/Wed, 18 Mar 2026 20:40:55 +0900https://sinimite.work/en/posts/rag-chunking-best-practices/A practical RAG chunking guide for AI application engineers, covering default parameters, mainstream splitting strategies, evaluation workflows, and production practices.https://sinimite.work/en/posts/agent-model-harness/Wed, 18 Mar 2026 00:17:00 +0900https://sinimite.work/en/posts/agent-model-harness/Starting from the formula Agent = Model + Harness, this article reframes the core work of an AI application engineer as harness engineering.https://sinimite.work/en/posts/prompt-engineering-best-practices-guide/Wed, 11 Mar 2026 11:00:00 +0900https://sinimite.work/en/posts/prompt-engineering-best-practices-guide/A systematic guide to production-grade Prompt Engineering and Context Engineering practices for 2025–2026, covering Claude, GPT, long context, Prompt Chaining, Tool Use, and evaluation workflows.https://sinimite.work/en/posts/anthropic-contextual-retrieval-reading-notes/Wed, 11 Mar 2026 09:00:00 +0900https://sinimite.work/en/posts/anthropic-contextual-retrieval-reading-notes/Reading notes on Anthropic's Contextual Retrieval, covering semantic loss after chunking, experimental benchmarks, ground-truth evaluation, and production RAG practices.https://sinimite.work/en/posts/contextual-retrieval-rag-reading-list/Mon, 09 Mar 2026 08:30:00 +0900https://sinimite.work/en/posts/contextual-retrieval-rag-reading-list/A Contextual Retrieval and RAG reading list for AI application engineers, covering official Anthropic resources, open-source implementations, evaluation methods, and industry trends.https://sinimite.work/en/posts/apple-m5-vs-m4-practical-comparison-ai-engineers/Fri, 06 Mar 2026 08:30:00 +0900https://sinimite.work/en/posts/apple-m5-vs-m4-practical-comparison-ai-engineers/An engineering comparison of M5 and M4 covering single- and multi-core performance, cache, unified memory, GPU AI acceleration, and the local-inference experience.https://sinimite.work/en/posts/prompt-engineering-from-concept-to-implementation/Tue, 03 Mar 2026 19:50:00 +0900https://sinimite.work/en/posts/prompt-engineering-from-concept-to-implementation/Prompt engineering? It may not be as simple as you think.https://sinimite.work/en/posts/understanding-prompt-injection/Tue, 03 Mar 2026 19:50:00 +0900https://sinimite.work/en/posts/understanding-prompt-injection/Understand LLM prompt injection and several fundamental defensive measures.