<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>ChainOfThought on SiBlog</title><link>https://sinimite.work/en/tags/chainofthought/</link><description>Recent content in ChainOfThought on SiBlog</description><image><title>SiBlog</title><url>https://sinimite.work/images/og-default.svg?v=20260525-210321</url><link>https://sinimite.work/images/og-default.svg?v=20260525-210321</link></image><generator>Hugo -- 0.156.0</generator><language>en-US</language><lastBuildDate>Sun, 24 May 2026 16:05:10 +0900</lastBuildDate><atom:link href="https://sinimite.work/en/tags/chainofthought/rss.xml" rel="self" type="application/rss+xml"/><item><title>What Did Tencent AI Leader Shunyu Yao Discuss in This Podcast?</title><link>https://sinimite.work/en/posts/points-of-the-podcast-language-agents-from-reasoning-to-acting/</link><pubDate>Sun, 24 May 2026 16:05:10 +0900</pubDate><guid>https://sinimite.work/en/posts/points-of-the-podcast-language-agents-from-reasoning-to-acting/</guid><description>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.</description></item><item><title>When an LLM's Inner State and Its Words Diverge: A Master Key to Understanding LLM Behavior</title><link>https://sinimite.work/en/posts/llm-from-hidden-state-to-token-output/</link><pubDate>Sat, 02 May 2026 00:00:00 +0000</pubDate><guid>https://sinimite.work/en/posts/llm-from-hidden-state-to-token-output/</guid><description>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.</description></item><item><title>The Complete Best-Practices Guide to Prompt Engineering</title><link>https://sinimite.work/en/posts/prompt-engineering-best-practices-guide/</link><pubDate>Wed, 11 Mar 2026 11:00:00 +0900</pubDate><guid>https://sinimite.work/en/posts/prompt-engineering-best-practices-guide/</guid><description>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.</description></item><item><title>The Complete Guide to LLM Chain-of-Thought (CoT)</title><link>https://sinimite.work/en/posts/llm-chain-of-thought-cot/</link><pubDate>Wed, 04 Mar 2026 21:51:00 +0900</pubDate><guid>https://sinimite.work/en/posts/llm-chain-of-thought-cot/</guid><description>Understand what LLM Chain-of-Thought (CoT) is and how prompt engineering can elicit Chain-of-Thought (CoT) from an LLM.</description></item></channel></rss>