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.

Demystifying Agentic Search Engines

Building Modern Recommendation Systems introduces a comprehensive, end-to-end pipeline that drives intelligent recommendations. The post walks through the full machine learning workflow — from raw data preparation and feature engineering to model training, deployment, real-time inference, and system monitoring. 

Modern Recommendation System Infrastructure

This post explores the full RecSys architecture, emphasizing the core models that drive each stage of the RecSys pipeline — from Retrieval for large-scale candidate generation, to Pre-ranking for efficient filtering, Ranking for fine-grained relevance modeling, and Re-ranking for balancing diversity and control.

Design a Modern Recommendation System

The ML Factory: Building Production ML Systems

本文简要总结了深度学习在NLP、计算机视觉、信息检索和推荐系统四大主流领域的演进脉络：从早期RNN、CNN等专用模型，到Transformer全面主导，再到如今BERT/GPT、ViT、Diffusion等预训练大模型横扫各领域。核心趋势是预训练+生成式范式取代传统任务特定模型，统一建模与生成式架构正在加速推动各领域融合与新一轮创新。

各领域的深度学习模型

大模型（LLM）关键技术：从基础到落地

机器学习模型：从传统算法到生成式AI

ML 模型生产全流程

NLP技术与应用：从语言理解到智能生成

模型训练的方法与实践

本文系统回顾了深度学习的发展脉络，从基础神经网络到Attention 与 Transformer的出现，再到深度生成模型的兴起，最后介绍了多模态与统一建模架构的发展趋势，展示了当前主流的模型体系。

深度学习模型架构的演进

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.

Retrieval-Augmented Generation (RAG)

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”。

对话系统：从人机交流走向理解与互动

Binary search is an efficient searching technique based on the divide-and-conquer principle. By repeatedly narrowing the search space, it guarantees a worst-case time complexity of O(log n). It is well-suited for sorted data, monotonic arrays, and optimization problems where the goal is to find the best value. Common use cases include exact matching, boundary and insertion point searches, finding the closest element, and performing binary search on the answer space. 

Shrinking the Search Space with Binary Search

The two-pointer technique is essential for optimizing operations on arrays, strings, and lists, often reducing time complexity from O(n²) to O(n). Common patterns include opposing pointers, sliding windows, fast–slow pointers, and dual-input pointers—each suited to different problem types such as finding pairs, subarrays, or merging sorted lists. 

Solving Problems with the Two-Pointers Technique

Topological Sorting Explained: Sorting Dependency Chains

Prefix search is a fundamental operation in computer science, typically implemented using a Trie (prefix tree). A Trie is a dynamic data structure for storing a collection of strings, supporting efficient insertion, lookup, and enumeration operations. Tries and their variants provide a powerful and efficient way to manage and query large volumes of string data.

Implementing Efficient Prefix Search with Tries

Depth-First Search: Exploring Deep Before Wide

Breadth-First Search: Level-Order Exploration

Recursion is a core computational concept where a problem is solved by calling itself on smaller instances. Recursion is key to many algorithms: DFS (Depth-First Search) is often implemented recursively, Dynamic Programming is fundamentally recursion with caching (memoization), and Divide & Conquer uses recursion to split problems into independent subproblems.

Understanding Recursion: Functions That Call Themselves

找出其中不含有重复字符的 最长子串的长度。

[Leetcode 3] 无重复字符的最长子串

找出并返回这两个升序数组的中位数 。

[Leetcode 4] 两个排序数组的中位数

找字符串 s 中最长的回文子串。

[Leetcode 5] 最长回文子串

找出数组 height中的两条线，使得它们与 x 轴共同构成的容器可以容纳最多的水。

[Leetcode 11] 盛最多水的容器

查找字符串数组中的最长公共前缀。

[Leetcode 14] 最长公共前缀

[Leetcode 17] 电话号码的字母组合

返回满足条件且不重复的四元组 [nums[a], nums[b], nums[c], nums[d]] 

[Leetcode 18] 四数之和

[Leetcode 19] 删除链表的倒数第 N 个节点

[Leetcode 20] 有效的括号

将两个升序链表合并为一个新的升序链表并返回

[Leetcode 21] 合并两个有序链表

将所有升序链表合并到一个升序链表中

[Leetcode 23] 合并 K 个升序链表

每 k 个节点一组进行翻转，返回修改后的链表。

[Leetcode 25] K 个一组翻转链表

找出最长有效（格式正确且连续）括号子串的长度

[Leetcode 32] 最长有效括号

找出给定目标值在排序数组中的开始位置和结束位置。

[Leetcode 34] 在排序数组中查找元素的第一个和最后一个位置

n 个柱子，下雨之后能接多少雨水。

[Leetcode 42] 接雨水

匹配字符串 (s) 和字符模式 (p), 支持 '?' 和 '*' 

[Leetcode 44] 通配符匹配

给定一个不含重复数字的数组 nums，返回其所有可能的全排列 。

[Leetcode 46] 全排列

位于网格左上角的机器人总共有多少条不同的路径达到网格的右下角

[Leetcode 62] 不同路径

[Leetcode 79] 单词搜索

删除已排序的链表中所有重复的元素

[Leetcode 83] 删除排序链表中的重复元素

柱状图能够勾勒出来的矩形的最大面积。

[Leetcode 84] 柱状图中最大的矩形

[Leetcode 88] 合并两个有序数组

[Leetcode 92] 反转链表II

[Leetcode 101] 对称二叉树

[Leetcode 105] 从前序与中序遍历序列构造二叉树

[Leetcode 116] 填充每个节点的下一个右侧节点指针

[Leetcode 121] 买卖股票的最佳时机

[Leetcode 122] 买卖股票的最好时间 II

[Leetcode 123] 买卖股票的最佳时机 III

[Leetcode 124] 二叉树中的最大路径和

[Leetcode 126] 单词接龙 II

[Leetcode 128] 最长连续序列

[Leetcode 138] 随机链表的复制

[Leetcode 143] 重排链表

[Leetcode 146] LRU 缓存

给出链表的头结点 head ，请将其按 升序 排列并返回 排序后的链表 。

[Leetcode148] 链表排序

[Leetcode 160] 相交链表

[Leetcode 171] Excel 列名转换为数字

[Leetcode 179] 最大数

[Leetcode 200] 岛屿数量

给定整数 n ，返回 所有小于非负整数 n 的质数的数量 。

[Leetcode 204] 质数计数

[Leetcode 206] 反转链表

实现 Trie 类：初始化、插入字符串 、检索、前缀检索

[Leetcode 208] 实现 Trie

给定一个二叉树, 找到该树中两个指定节点的最近公共祖先。

[Leetcode 236] 二叉树的最近公共祖先 

大小为 k 的滑动窗口从数组的最左侧移动到数组的最右侧，返回 滑动窗口中的最大值 。

[Leetcode 204] 质数计数

解法一：试除更小的质数

解法二：埃氏筛

解法三：线性筛（欧拉筛）

解法四：分段筛（Segmented Sieve）