LLM

Demystifying Agentic Search Engines

Agentic search engines—such as Google AI Mode, Perplexity, Bing Copilot, ChatGPT Search no longer means “type keywords, get ten blue links.” AI Search experience capable of understanding tasks, planning queries, calling tools, and synthesizing results and deliver a conversational response with inline citations, minimizing user effort. In this post, I’ll walk through the stack from bottom to top, how it crawls and indexes pages, how it retrieves and ranks information, and how recent features like RAG and Agentic search build upon these foundations.

大模型（LLM）关键技术：从基础到落地

Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) combines large language models with external knowledge retrieval to produce more accurate and grounded responses. The post explains why RAG was introduced, explores its key use cases and real-world applications, and discusses challenges and considerations that impact performance in practical deployments.

对话系统：从人机交流走向理解与互动

本文探讨了机器学习如何推动人与机器的自然交流，从早期的对话系统到如今能够理解意图、执行任务的智能助理。近年来的趋势是向LLM + Agent 化对话系统演进，LLM 可嵌入架构中各核心模块，增强系统的理解、生成与决策能力。最终，通过引入智能代理机制，让对话系统从“能说”进一步迈向“能做”。