https://sinimite.work/en/tags/llm/Recent content in LLM on SiBloghttps://sinimite.work/images/og-default.svg?v=20260525-210321https://sinimite.work/images/og-default.svg?v=20260525-210321Hugo -- 0.156.0en-USThu, 16 Jul 2026 21:08:52 +0900https://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/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-from-hidden-state-to-token-output/Sat, 02 May 2026 00:00:00 +0000https://sinimite.work/en/posts/llm-from-hidden-state-to-token-output/Why does Chain of Thought work? Why are longer prompts not always better? Why do models hallucinate? These apparently unrelated engineering phenomena are different expressions of the same core mechanism. This article uses one master key to open five doors in LLM engineering.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/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/entropy-in-llm/Fri, 27 Mar 2026 00:00:00 +0000https://sinimite.work/en/posts/entropy-in-llm/Entropy runs through the entire LLM lifecycle: training uses cross-entropy as the loss, inference uses temperature to control output entropy, evaluation uses perplexity, and products can use entropy signals to detect hallucinations. This article maps the complete picture from an AI engineer's perspective.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/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/llm-api-kv-cache/Thu, 05 Mar 2026 11:09:00 +0900https://sinimite.work/en/posts/llm-api-kv-cache/KV Cache is a key concept connecting Transformer theory with LLM engineering and deployment. Understanding it completes the path from how a model computes to how it runs.https://sinimite.work/en/posts/llm-chain-of-thought-cot/Wed, 04 Mar 2026 21:51:00 +0900https://sinimite.work/en/posts/llm-chain-of-thought-cot/Understand what LLM Chain-of-Thought (CoT) is and how prompt engineering can elicit Chain-of-Thought (CoT) from an LLM.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.https://sinimite.work/en/posts/understanding-transformer-intuition/Tue, 24 Feb 2026 12:00:00 +0900https://sinimite.work/en/posts/understanding-transformer-intuition/My first formal blog post.