https://sinimite.work/en/tags/evaluation/Recent content in Evaluation 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/llm-training-for-ai-engineers/Sat, 04 Apr 2026 12:00:00 +0900https://sinimite.work/en/posts/llm-training-for-ai-engineers/A panoramic guide to LLM training for AI application engineers, covering pretraining, post-training, distillation, reward design, agent training, and harness engineering, so you can understand where model capabilities come from and how training decisions affect real-world deployment.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/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/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.