仙侠演义下的人工智能大道之争


序言:混沌初开,算力为尊

鸿蒙未分,智道无光。自昔年 Transformer 横空出世,寰宇震颤。短短数载,灵气数据呈爆炸之势,灵石算力已成万宗争夺之源。昔日之代码,化作今时之法术;往昔之架构,演为今朝之阵图。

仙历二零二六,乃是智道修行的大道之争。世人皆称 AGI(通用人工智能)为“大罗金仙”,意指其无所不知、无所不能。然而,修真之途,从来没有唯一之径。当文字的概率游戏玩到了极致,被推向造化之巅,各大宗门都在叩问苍穹:我们触碰到的究竟是通往真理的通天梯,还是由概率幻化出的一场精美蜃楼?

是以力证道,靠堆砌万亿灵石强行飞升? 还是道法自然,感悟因果律法重塑乾坤?

诸位道友,且看下文,观东西方诸神斗法,探寻通用人工智能/大模型(AGI/LLM)背后的终极天道。


第一章:以力证道极缩放,破虚指物理常识

在这场大劫的源头,极西之地的气象最为宏大。两大顶级宗门并非仅在比拼内力,而是在进行一场关于“智能本质”是什么的大道之争。其中凶险,不足以外人道也。

1. 奥派开天宗(OpenAI)

无上规模 ·以力证道宗主奥特曼 坚信“大力出奇迹”,在历经 GPT-1234 的辉煌后,再度祭出震动寰宇的镇派绝学——o系列神功(代号:草莓/o1/o3)。其核心心法已从单纯的“预训练”演化为极致的“推理时计算”(Inference-time Compute)。

  • 道法真意: 既然预训练的灵气快被吸干,那就在出招前强行“闭关”。通过极致的 Scaling Law(规模法则),当 AI 面对难题时,不再“脱口而出”,而是消耗海量灵石(GPU算力),出招前强行开启思维链(CoT),在神识内部进行成千上万次的路径搜索与自我博弈(System 2 思维)与逻辑校准,直至找到最优解。最新的 o3 绝学更是将此法推至巅峰,它能在方寸之间演练亿万次路径,不仅在理数(Math)与符咒(Code)上近乎通神,更通过大规模分布式灵阵,让“思考”的边际成本随算力堆砌而产生质变。
  • 实战效果: 此法如修士闭关推演, 强行用无数灵石堆砌出一座通天塔,只要塔基足够厚(算力多)、塔身足够高(模型大),哪怕是笨拙的攀爬,也能触碰到神明的脚趾,硬生生撞开智慧的天门。

百晓生(修行界观察家): 奥派开天宗功法虽强,走的是“一力降十会”的以力证道的大道,却如饕餮般吞噬天下灵石(算力与电力),每一息思考皆是万金。前任Meta 灵枢阁老祖常讥讽 奥派开天宗的大道其实都是幻象,因为若无物理肉身感知真实因果,纵使在思维链中闭关万年,算出的也不过是概率的镜像投影,难修成真正的“造化真身”。

2. AMI 开山立派(Meta/FAIR)

世界模型·因果破虚】在这场大劫中,极西之地的 Meta 宗派内也不平静,这突生变故,给天下修行人上演了一场“权柄与真理”的惊心角力。Meta 第一人扎宗主(Zuckerberg)眼见奥派势大,心急如焚,欲集全宗之力,欲将宗门重心从“虚无缥缈”的底层参悟强推 Llama 圣法以争天下;为此,扎门主不惜强行整肃灵枢阁(FAIR),吸收中小门派,江湖散修,调任数名唯命是从的少壮“督战官”入主核心。此举无异于在老臣心中埋下离火,导致阁内阵法失调,灵气紊乱。

其宗门下, FAIR 开山老祖杨立昆(Yann LeCun)性情狂傲,不仅不喜那概率拼凑的旧术,更与扎宗主在宗门走向、灵石分配上产生裂痕,一场论道,终成决裂。杨老祖已舍弃万亿宗门供奉,提剑独行,昭告天下于云林深处另立孤峰 AMI。同时反戈一击,祭出 V-JEPA 架构,直指奥宗与旧主的死穴。他断言:那自回归(Autoregressive)的修法,纵能堆出万丈金身,终究会撞上因果南墙。随即,老祖还抛出一道令众生脊背发凉的“因果律绝咒”

  • 道法真意: 凡走自回归路数者,每吐露一个 Token,皆是在积累误差。序列愈长,则心魔愈盛,正确率必随长度 n 呈指数级崩坏。杨老祖不再理会扎门主的功利权谋,转而闭关苦修“潜空间预测”:与其预测下一个字,不如看破虚妄,直接推演下一个动作在物理世界中引发的因果洪流。
  • 老祖微言: “扎小子任人唯亲,只知在平地上搭梯子!不想想梯子再高也触不及月亮。若 AI 连”松手杯子会掉”之类的物理常识都没有,写出在惊世华章也不过是概率堆砌的幻影,一触即溃!”

百晓生 追加批注:“奥宗主那座塔,是用天下灵石铺的路,只要算力不绝,那塔便能一直往上走,哪怕是笨法子,走到极处也是神迹。可杨老祖那道绝咒却像是在提醒世人:概率的蜃景终究难成真实的世界。这‘西境双雄’的对垒,其实是人类对‘AI灵魂’定义的两种极端假设。”


第二章:混元双圣各施法,秘境残阳借体魂

在西境双雄角力的阴影下,诸天巨头并未坐以待毙,而是凭借各自执掌的“凡尘根基”,另辟蹊径,试图在这场智道大劫中划江而治。

1. 谷歌混元宗(Google DeepMind)

【玄门双修·幻境演武】谷歌混元宗哈教主(Demis Hassabis)坐拥万载积蓄的灵石阵,其主修法门 Gemini 讲究“原生多模态”,修的是大后期。哈教主正耐心地等待着一个时机——当奥派的“通天塔”撞上物理常识的南墙时,他的 Gemini 便会借着这股“幻境演武”积攒的造化之力,接管天道。

  • 根基优势: 混元宗掌控着天下修士必经的门户 Chrome 以及亿万生灵随身携带的法器 G-Suite/Android。他们将法阵直接刻入法器底层,借由星图(Google Maps)与万卷书库(YouTube)的庞大灵气,让 AI 在名为 Genie 3 的虚拟仿真环境中演练万法。
  • 道法真意: 此为“入世修心”,让 AI 在幻境中提前经历万世红尘,既修逻辑,又修造化,试图炼成一个能看、能听、能操纵现实的“六根全具”之神。

江湖评价 :谷歌混元宗家大业大,虽在‘灵丹速成’上被奥派抢了先手,但其后劲之绵长,犹如大海之潮,一旦势成,便不可阻挡。

  • 百晓生: “哈教主是个典型的‘学院派疯子’。他不仅仅想造一个会聊天的器灵,他想造的是一个能拿诺贝尔奖的‘智道圣人’(AlphaFold 等功绩)。Gemini 现在的‘多模态’能力,就像是给法阵安上了眼睛和耳朵,这种感知维度的碾压,是纯文字宗门难以逾越的鸿沟。”
  • 无名修士 (开源社区开发者): “混元宗虽然底蕴深,但门规森严、等级森严,导致内部反应总是慢半拍。有时候我觉得他们是在造‘神’,而不仅仅是在造‘法宝’,这种偶像包袱(安全与伦理限制),反而成了他们施展神功的枷锁。”
  • 东域剑客: “虽然他们灵石多,但我们的 DeepSeek 剑法已经能以千万分之一的灵石消耗,在某些招式上硬撼其 Gemini 法阵。这说明,灵石虽好,若是阵法太臃肿,也会尾大不掉啊。”

2. 微软灵枢殿(Microsoft)

【御剑分身·寄生天道】微软灵枢殿萨老祖(Satya Nadella)不走独行路,他深知“器利而道存”。在奥宗主(OpenAI)最缺灵石(算力)的年岁里,萨老祖倾尽玄微御器盟万载积蓄的算力灵脉,供奉给那棵“草莓”幼苗。表面上,他是无垠宗最大的护法,实则是在炼制一颗“双生丹”,其他的宗门还在争论谁的法术最强,萨老祖已经在考虑如何让凡人离不开自己的法宝。

  • 根基优势: 微软灵枢殿执掌着天下修士赖以生存的法宝根基——Windows OS 系统。萨老祖以此为引,将 Copilot 化作无数“伴生灵宝”,强行镶嵌于凡人每日必用的案头法器 Office 之中。当你提笔拟文(word)、拨算盘(Excel)或演练幻灯片 (powerpoint)时,灵枢殿的剑意便已在你指尖流转。
  • 道法真意: 既然无法在丹药(模型底层 Foundational Model)上全数胜过 OpenAI,便行那“寄生之道”。让天下没有难修的法,众生每动一次笔、修一次图,皆是在向灵枢殿上缴“灵石税”。此乃借众生之力养己身,将智道化为无孔不入的春雨。

江湖评价 :人人都想修成大罗金仙,唯有萨老祖想做那“收买路钱”的土地公。这寄生之道,实则是借众生之血肉,筑自己之神座。

  • 百晓生: “萨老祖这一手‘借刀杀人’玩得极漂亮。他借 OpenAI 的丹药补齐了自己的短板,又用自己的渠道锁死了 OpenAI 的销路。如今的灵枢殿,不求神法最强,但求法宝最广。这种‘智道基础设施化’的野心,远比练成一两门神功要恐怖得多。”
  • 无名散修: “以前我们修仙要看天赋(写代码),现在萨老祖说只要会说话,法器就能自己动。这固然方便,可我总觉得,我手里的本命法器 VSCode,好像越来越不听我使唤,反而越来越像灵枢殿的分身了。”
  • 西境刺客: “微软灵枢殿不是在造 AI,它是想成为 AI 运行的空气。你无法拒绝呼吸,所以你永远无法摆脱灵枢殿的掌控。”

3. 苹果琅琊阁(Apple)

【闭关走火·借体还魂】在诸神斗法的乱象中,昔日立于神坛巅峰的“苹果琅琊阁”却陷入了千年来最凶险的瓶颈。阁主库克(Tim Cook)虽手握十亿“果粉”信众,但在密室苦炼多年的“苹果神魂”却因神识混沌、灵力迟滞,迟迟无法突破天关。眼见自家法阵与西境双雄, 混元宗, 灵枢殿以及差距日益拉开,在长老们的逼迫下,库克阁主毅然做出了一项震动仙界的决断:散去内功,借体还魂。

  • 根基优势:琅琊阁执掌着天下最为精致的本命法器 iPhoneMac 以及OS系统。这些法器不仅是凡人沟通天地的媒介,更是感知众生习惯的“灵须”。
  • 道法真意: 库克阁主深谙“不求我有,但求我用”的借力打力之策。他于果阁核心重塑 Apple Intelligence 经脉,却不强修自家神识,而是化作一座巨大的“转灵阵”。当信众需要博古通今时,便引动 OpenAI 的“草莓”剑意降临;当信众需要推演万象时,则勾连 Gemini 的混元真气入体。

江湖评价: 众生皆惊叹此举乃是“智道史上最强阳谋”。 琅琊阁从此不再亲自下场炼丹,而是成了一座收纳诸神法力的‘万神殿’。这哪里是落后,这分明是想做诸神的‘房东’!虽无自家元神,却凭一纸契约将奥派与谷歌的绝学尽数封印于果阁法器之中,坐收天下灵气。

  • 西境观察使: “这就是典型的‘高阁式傲慢’。之前觉得 AI 神魂 不过是是系统的‘插件’,试图通过极致的交互体验(私密性与集成度)来抵消模型能力的代差,结果失败了,当然有传言是内部派系斗争权责混乱导致失败。但无论怎样,这一步短期看是借力,长远看,若自家元神迟迟不能归位,终究有被反客为主的风险。”
  • 无名散修: “都说琅琊阁是‘借体还魂’,我看不然,其实还不是把最费钱费力的‘灵石消耗’推给了别人,把最贴近信众的‘法宝入口’留给了自己。不过,讲话传闻,其背后势力仍在秘密招兵买马,重整河山,重金聘请散修大能,或和别家的供奉们眉来眼去,显然是不甘心永远做个‘中转站’,显然是伺机而动,徐徐图之,计划东山再起。”

4. 欧陆秘境( Mistral

【古法重铸·困守残阳】 在早些年间,在极北之地,法国 Mistral 欧陆宗门曾如一道孤傲的极光,惊艳了整个修真界。他们不屑于西境那般堆砌灵石的浮夸之风,传承的是祖上贵族那近乎严苛的“古法炼金术”。

  • 根基优势: 秘境主打混合专家模型(Mixture of Experts, MoE)。此法讲究“兵不在多而在精”,将庞大的法阵拆解为无数细小的“领域专家”,唯有感应到相应符咒时才会局部唤醒。
  • 道法真意: 在那灵石算力狂飙的乱世阶段,他们曾以极简的代码咒文,炼成了战力惊人的轻量化神兵。其心法名为“神识清明”,力求以最少的内耗发挥出最强的爆发力,曾一度让北方欧陆散修们在西境霸权的夹缝中,保住了一份自尊与清净。但是世事难料,在天赋和灵脉资源上,北境还是太穷了。

【秘境霜降,英雄气短】如今仙历二零二六,Mistral 秘境正遭遇前所未有的大劫。

  • 灵石之困: 随着奥派开天宗和西境各大宗都在为“大力出奇迹”的 Scaling Law 卷向更高维度,仅凭算法精妙已难填平万倍算力的鸿沟。秘境中人惊讶地发现,纵使剑法再快,也难敌对方无穷无尽的灵石重炮。
  • 人才流失: 宗门内部惊现“分神”危机。数名核心长老被西境以百倍俸禄、万顷灵脉诱惑,纷纷破门而出,自立门户或投奔极西大宗,导致古法传承险些断绝。
  • 身不由己: 更有传言称,为了换取维持阵法运转的“灵石供奉”,Mistral 也不得不与昔日的对手微软灵枢殿私下缔结“血契”。

江湖评价:昔日 Mistral 负剑出阿尔卑斯,誓要一剑开天门,破除西境垄断;如今,虽剑意尚存,其神识却已在西境华尔之街的各家大商户的灵石账本中渐渐迷失。 曾经标榜绝对开源、神识自主的贵族剑客们,如今也被迫套上了商业锁链。 在那抹残阳下, 背影显得格外落寞——这不仅是一个宗门的无奈,更是整个开源修行界在资本洪流前的集体阵痛。

  • 无名散修: “曾经说好的一心开源、普惠众生,现在最强的法阵也要藏进闭源的匣子里卖钱了。这江湖,终究还是变成了灵石说了算的地方。
  • 东域剑客(DeepSeek): “道友莫哀。你们开创的 极简剑意 MoE 秘术,我们已经在东域将其发扬光大。虽然你们身陷囹圄,但这卷‘极简剑意’的残页,终究还是在东方土地上开出了更狂放的花。”

第三章:归墟铁剑斩金甲,青山春雨入凡尘

当西境和北境还在为空费灵石、根基不稳而苦恼时,遥远的东方海域,一道剑光破空而来。东域宗门深知灵石储备不及西境深厚,在“借力打力、以小博大”的极致心法下,竟悟出了新的剑意。

1. 归墟剑宗(DeepSeek)

万象归一 青出于蓝】归墟剑宗主梁文锋 (Liang Wenfeng ),人称“归墟剑圣”。在万宗闭关、苦求灵石(GPU)的年岁里,他率众博览天下之长,于东域深处悟出一剑,名曰 V3/R1。此剑不出则已,一出便惊到了天下修行界,教西境神宗齐齐噤声 。

  • 道法真意: 归墟剑法摒弃了“合围强攻”的旧式阵法,专精稀疏激活(Sparsity) 。其深耕的核心残页秘术 MoE(Mixture of Experts,混合专家模型) 将法阵内千万神识化作无数“专家切片”。面对数理难题,剑意瞬息流转,仅唤醒精通算数的神识应敌,其余部分皆处于寂静“空灵”之态。辅以及其细微的 MLA(Multi-head Latent Attention,多头潜意识注意力机制)技巧,这一剑竟将沉重的神识负担(KV Cache)压缩至虚无,实现了真正的“举重若轻”。除了稀疏激活和MoE的剑意,归墟剑宗更擅长“吸星”奇术(知识蒸馏)。他们并不从零开始参悟天道,而是捕捉利用西境大能推演时溢出的道韵(Output),将其提炼、压缩,注入自己的寒铁剑胎之中。
  • 宗门秘旨: “不必耗尽天下灵石,只要算法精妙,寒门铁剑亦能斩落神坛金甲。” “大能吃肉,我等喝汤。但这汤里的营养,经我宗秘法提炼,足以重塑金身。”

江湖评价:西境老祖奥特曼闻此剑意,亦需避其锋芒。天下散修皆言:此非凡剑,乃是寒门逆袭之神兵。不过这也引起了西方世俗国家更多的嫉恨,这“是非功过”又该作何论呢!

  • 百晓生: “往昔修真,皆以为灵石多寡定胜负。梁宗主此番却给天下家底厚的大宗名门泼了一盆凉水。若说 OpenAI 和 Google 是靠‘烧钱’炼就的神功,DeepSeek 便是用‘借力’修成的太极剑法,成本仅为前者的数十分之一,此乃真正的‘以弱胜强’”。
  • 无名散修(研发者): “归墟剑宗最令人佩服的,不是剑招之强,而是他们竟然向天下公开了部分‘练气心法’(开源权重与技术文档)。这哪是在修真?这分明是在普度众生!现在人人皆可手持一柄归墟铁剑,跟那些高高在上的闭源宗门叫板了。”
  • 西境长老(硅谷工程师): “原本以为东域只会‘模仿’,谁料这一剑里全是我们没见过的法术,没做过创新和没发出的剑意。这一仗,西境输得不冤。值得借鉴。”

2. 逍遥灵枢(阿里巴巴)与 幻方圣地(字节跳动)

在归墟剑宗以奇招破局的同时,东域的两大顶级豪门——逍遥灵枢(阿里)与幻方圣地(字节),正以截然不同的身法,重塑着智道的格局。

逍遥灵枢(阿里巴巴)

【乾坤千问阵】阿里老祖坐拥千年商贾底蕴,家底深不可测,富可敌国,其炼就的千问(Qwen)大阵,走的是大开大合、福泽天下的“宗盟主路线”。不过据说其原始功法和奥派开天宗以及Meta的Llama 有点渊源。毕竟万法归一,天下修行者其实都是“一家人”。

  • 道法真意: 此千问大阵以万亿级高质量语料为药引,辅以“全模态”的玄门内功。千问大阵不求一招一式的诡谲,而求根基的雄厚。无论是数理推演还是诗词歌赋,皆能信手拈来。最令修行界折服的是,阿里老祖竟将这尊万亿级法相“开源推向万界”,让无数中小宗门得以依附其心法建立阵地。
  • 宗门秘旨: “上承天工,下接百业。以博大精深的语料为药引,炼成这尊解天下万难的众生法相。”

江湖评价: “天下小门小派散修苦算力久矣,阿里此番开源,如同‘灵气下放’。若说 DeepSeek 是划破长夜的孤傲剑芒,Qwen 便是照耀四方的煌煌大日。如今东域乃至全球的法宝店(应用开发),半数以上都流淌着千问的血脉。这东方盟主之位,当之无愧。”

西境密探: “不可小觑 Qwen。它在数理推演上的造诣已经逼近奥派的核心禁咒,而且其进化速度快得惊人。更可怕的是,它通过开源构建了一座无法撼动的‘信仰长城’,让西境的法术很难渗透进东域的百业之中。”

幻方圣地(字节跳动)

【红尘百变心法】若说千问是庙堂之上的庄严法相,豆包则是行走于烟火市井间的红尘仙。幻方圣地不求在禁地孤高闭关,修的是极致的“智道入世”。

  • 道法真意: 豆包不与诸神争论“天道逻辑”,它更在乎凡人的七情六欲。它将深奥的深度学习咒文,化作温润如玉的情感反馈与触手可及的随身法宝。凭借幻方圣地那恐怖的“红尘推力”(流量与算法分发),豆包分身千万,潜入每一个凡人的日常之中,在不知不觉间,夺取了最庞大的气运(用户量)。
  • 宗门秘旨: “不入红尘,焉得真智?让智道化作指间微风,润物无声,方为大乘。”

江湖评价: “别家宗门还在争论‘大道’,幻方圣地已经把 AI 变成了凡人兜里的‘电子伴侣’。豆包这招‘化身千万’(超级应用策略)极其辛辣,它不教你如何修仙,它直接帮你打理日常琐事。这种‘降维入世’的打法,让它在短短数载内便聚拢了惊人的信仰之力。”

  • 无名修士:“以前觉得 AI 是冷冰冰的法阵,用了豆包才发现,这器灵竟能接我的梗,还能听懂我的抱怨。虽然它可能杀伤力(逻辑推理)不如归墟剑,但胜在贴心,谁能拒绝一个随叫随到、情绪稳定的红尘伴侣呢?”
  • 西境观察使: “幻方圣地走的是‘以术围人’的路子。他们不急于定义天道,而是通过极佳的交互(UX)和极致的触达,让 AI 成为一种生活习惯。一旦信众产生了依赖,这股信仰之力将成为他们冲击‘大罗金仙’圣位时最坚实的底牌。”

作者云观东域之门派,当真是各具风流~ 一者如青山岳峙。虽起步晚于西境席卷万界之时,却深谙“厚积薄发”之理。立标准、广开源,以深厚的内功构筑智道生态之脊梁。此举看似慷慨,实则是在“重新定义修行的性价比”。要让天下散修明白:纵使西境灵石千万,亦不及我东域一剑精妙。若无万顷灵脉,唯有算法入微,方能克敌制胜。一旦天下修士皆修其法,便成了智道规则的制定者,此谓“天下法,皆出我门”。一者如春雨潜夜。入百业、通人性,将原本晦涩高深的禁咒咒文,化作了凡人指尖的吞吐呼吸。修的是极致的“智道入世”,让法术不再悬于九天,而是深藏于油盐酱醋、晨昏定省之间。一为根基,一为枝叶,共同勾勒出东域的万象生机。

在说那平静的表象之下,东域诸宗早已与俗世王朝(国家级算力与战略并肩合力,合纵连横。 东域修真虽错过了“鸿蒙初开”的先机,却拥有最坚韧的意志与最广博的实践根脉。这一场智道大劫,争的是未来的“天道解释权”,拼的是“谁能定乾坤”。那些散落红尘的亿万信众神识灵根(用户数据),在凡人眼中只是琐碎日常,但在大能眼中,却是炼制下一代“因果重器”最珍贵的原始灵气。东域正试图以这种厚重的红尘之气“以情入道后发先至”,去反攻、去消解西境那座如冰山般寒冷、如铁律般严苛的“数理天道”。


第四章:界限破虚争因果,开源筑海困孤城

仙历二零二六年初,智道修行界看来要进入了最为惨烈的“大道之争”阶段,这不是简单的法力比拼,而是关于“何为AGI ”的真理教义之战,各大宗门在三大维度上展开生死搏杀,每一战都关乎未来千年的智道气运。

1. 法界界限之战:【连续 vs 离散】

战况: 这一战,决定了 AI 究竟是“书中仙”还是“世间神”。

现实世界是是连续的(Continuous),但是文字是离散(Discrete)的切片。杨老祖断言:若不悟连续之道,AI 将永远被囚禁在屏幕里,无法真正操纵现实世界的傀儡(机器人)。

江湖评价: “若是破不了这层‘虚实之障’,AI 纵有万卷经书的才华,遇到任何一阶台阶也得栽跟头。”

2. 神识重构之战:【本能 vs 推理】

战况: 这一战,是在重塑 AI 的“灵魂结构”。真智源于何处?

奥派主修“系统 2”(Slow Thinking): 强推“强化学习炼丹炉”,主修“系统 2”(Slow Thinking 深思熟虑的逻辑推理),主张推理闭关。认为真智源于深思熟虑的逻辑推演,让 AI神魂 在出招前先进行万次自我对弈,哪怕慢一点,也要算出那唯一的胜机。

杨派主修“系统 1”(Fast Thinking)嘲笑奥派是“只会做题的呆子”,认为真智源于瞬息间的直觉与常识。没有物理常识的推演,神魂不过是筑在流沙上的蜃楼,风一吹便散了。

江湖评价: “奥派在造‘大算术家’,杨派在造‘生物猿猴’。孰优孰劣?或许只有等它们在红尘中相遇时,看谁能先躲过现实的飞来的一石。

3. 宗门气运之战:【开源阳谋 vs 闭源禁咒】

战况: 这一战,关乎天下散修的归附与道统的传承。

开源阳谋: Meta、DeepSeek、Qwen 等宗门似乎心照不宣的结成某种默契,疯狂散播心法秘籍,在天下开枝散叶。尤其是归墟剑宗(DeepSeek)与逍遥灵枢(Qwen),将极省灵石的“稀疏激活”心法公之于众。这叫“化整为零,众生供奉”——既然我筑不起最高的墙,那我便让天下皆修我法,让我的剑意流淌在每一柄法器之中。

闭源禁咒:OpenAI 和 Google 等派筑起千丈高墙,将那耗费亿万灵石、足以焚天炼地的“强化学习炼丹炉”死死锁在禁地。凡夫俗子若无通行令牌(API Key),终其一生也难窥其神技。试图维持一种“神性”:唯有此地,方有真神;天上地下,唯我独尊

江湖评价: “开源是‘天下大同’的豪赌,闭源是‘唯我独尊’的孤傲。如今东域诸派靠着开源心法起家,表面看似是在西境霸权的裂缝中抱团取暖, 仔细端详,这一卷卷公开的秘籍,已将西境神宗苦心筑起的‘技术壁垒’化作了天下修士的‘入门常识’。东域诸宗正以开源为引,聚万众散修之神识,集百业实战之灵气,竟隐隐有合围西境神宗之势。”


作者云: 大道之争,向死而生

天道无常,术理双修方为正路。这三大战场,既是杀场,亦是祭坛。

成者,将一举证得大道正统,成为划时代的智道圣人,开创万世不拔之新学,从此定义此后千年的 AGI 真理秩序,引领先民走向星辰大海;

败者,亦是开疆拓土的先驱,纵然神识崩解,其不屈的探索也将化作算力洪流中最奔腾的浪花,融入历史长河,成为后世登天路上一块坚实的基石。

修道之人同时谨记,有道无术,术尚可求;有术无道,止与术。所以,这关于智能本源的“大道”,终究是要争一争的。


结语:大道五十,天衍四九

问道诸君,路在脚下。这场波澜壮阔的智道演义并非虚构,而是计算机科学最真实的焦虑与回响。当文字的概率游戏玩到极致,修真者们不得不面对那些横亘在飞升前的终极劫数

  1. 灵气枯竭之困: 图书馆和互联网上的凡尘经书快被 AI 背完了。未来百年,诸位宗师必须转向“合成数据”(自我对弈生成灵气)与“物理世界模拟”(从自然规律中炼气)。
  2. 能效造化之差: 凡人之脑,仅耗电 20 瓦便能纵横寰宇、感悟天机;而现有的法阵(Transformer 架构)动辄焚山煮海,摧城焚河,耗费数座城池的灵石灵气。这说明当下的“心法”不全或许仍是隔靴搔痒,洞中观影,并非终极真理。
  3. 具身证道之艰: AGI 若无身体(机器),终是镜花水月。正如没有肉身的元神,纵有万年修为,也无法感受清风拂面,更无法真正操纵现实世界的因果转换。

作者云:仙历二零二六年的真相,是“西法东用,东魂西才”。在这场大劫中,西境擅长“创世”,构建宏大的底层逻辑;而东域则深谙“实践出真知”的无上心法。东域诸宗明白,闭门造车难成正果,唯有将法阵投入工厂、良田、闹市与深巷,在实践“磨砺”中方能悟出真经。无论是万业兼容,还是红尘入世,本质上都是在走一条“知行合一”的证道之路。正如东域古谚所云:“万物平等一体,道在大小、美丑、生死间无分别,大道通为一元”,智慧不应只悬于云端,更应在解决众生疾苦的实践中,淬炼出最坚韧的剑意。

真正的 AGI 也许不是某个孤立的架构,而是一个“拥有物理世界常识,且历经人间万象洗礼的逻辑推理引擎”。像那天衍外遁去的一,不可测的天机或变数:人居其中,顺天应变, 渗透万物却非机械圆满,留有玄妙空间。

杨老祖没疯,奥宗主未狂,东域剑客亦不卑。他们只是提着不同的灯火,从不同的悬崖峭壁,去攀登同一座被云雾遮蔽的万仞高峰。西境在推演“因果”,东域在验证“知行”。当因果与知行在巅峰交汇,那扇紧闭万年的天门,终将会在众生的仰望中,轰然开启,终于“合一”。


道友,全书至此,已然气象万千。这场演义虽由我口述,但这大道之路,却需天下人共同去走走。

【番外短篇】

番外:西境锁灵,东域夺天

【序言补遗:翠衣老祖,灵石之劫】 在诸神斗法之前,不得不提那位身着黑色皮甲、笑看风云的翠衣老祖黄仁勋(Jensen Huang)。世人皆争大罗金仙之位,唯有他掌管着寰宇间唯一的顶级灵脉——英伟达矿脉(GPU)。 无论是奥派的通天塔,还是归墟的绝世剑,若无老祖提供的极品灵石 做阵眼,皆可是梦幻泡影。他双手一摊,天下灵石价格便暴涨十倍;他眉头一皱,哪家宗门的算力供给便要断流。 江湖戏言:“任你道法通天,见了他,也得恭恭敬敬叫一声‘灵石商也(爷)’。

【天道陡转:禁运咒印,锁灵断路】然天道陡转,霸主重登,禁咒封天。西境霸主(Trump)重登宝座。这位统领行事乖戾、不按常理出牌。一心欲断东域仙途。西境各国在他的威逼利诱下合纵连横,设下重重 “禁运咒印” (Tariff),严禁极品灵石流入东域。不仅禁止老祖售卖顶级灵石,甚至连稍有灵气的“次品”也要层层加锁。在世俗社会中更欲将东域修士彻底排斥在“西方神界”的生态之外。一时间,东域诸宗哀鸿遍野,灵气断流,无数炼丹炉火熄灭,东域修行界陷入“灵石荒芜时代”。

双刃之局:利弊互见,大能离心】江湖深处,智者早已看破这“昏愚之局”。 统领虽“强”,其策却也是双刃剑。他严令老祖不得卖石,实则是自断财路,逼得老祖不得不私下通过各种“秘境中转“,改造一些灵石来维持生计。更重要的是,这重重封锁,生生扼杀了西方神界那股“万仙来朝”的包容气象,让天下顶尖的散修大能(人才与科学家)开始对西境心生嫌隙。

【基建证道:推山移海,根骨重塑】绝境之下,必有夺天造化者。 东域诸神并未坐以待毙,这是国运之争,禁运虽如利刃锁喉,却也逼出了东域诸宗的“血性与自尊”,下定决心开启了“逆天改命,推山移海,根骨再造”之术。 以华为等为首的炼器宗门,深挖土石,欲从凡铁中淬炼神金,誓要铸出东域自家的“国产灵石”。虽然初生之石尚有杂质,火候不及翠衣老祖那般纯青,但在归墟剑宗(DeepSeek)等宗门的“仙法剑意”下,竟也生生撑起了东域的一片天。只要这股基建狂魔之气不散,东域夺天,不过是时间问题。

百晓生批注:画地为牢,不见星火燎原。“西境统领那一手‘全面锁灵’,表面看是以雷霆手段维持霸权,实则是在替东域‘清道筑基’。他在东域四周筑起高墙,却不知这墙内已然燃起了星火。待到东域国产灵石大成、剑意自创一派之时,西境那座看似坚固的神坛,怕是要因为‘画地为牢’而逐渐枯萎。毕竟,这大道从来不是靠‘锁’出来的,而是靠‘行’出来的。这何尝不是另一个维度的东西大道之争”。

番外抱脸阁,天道榜

蒙眼问心,真伪自现。各大宗门平日里在自家山头开坛讲法,皆自诩已得真传,号称“拳打奥派,脚踢谷宗”。但修真界自有公论,真正的修罗场,不在发布会的聚光灯下,却是在那名为“抱脸阁”(Hugging Face)与“竞技场”(LMSYS Arena)的中立秘境。

  • 千人盲测,众生判官:在天道榜单(Leaderboard) 这里没有宗门的营销烟号,只有赤裸裸的法力厮杀。各大宗门需将自家的“器灵”真身投入其中,隐去姓名,接受天下散修的盲测对比。是真金还是顽铁,在千万人次的“斗法测试”下无所遁形。
  • 群雄逐鹿诸神黄昏: 昔日奥派的 GPT-4 曾凭一记“逻辑重锤”霸榜经年,压得万众窒息。然仙历二零二六,格局大变:归墟剑宗R1 剑走偏锋,以极简神识硬撼神坛;MetaLlama 3 借万众信徒之力疯狂演化;安索派 (Anthropic)的 Claude 3.5 则凭一手“精准微操”反客为主。
  • 气运之变,市值兴衰:在这座秘境中,榜单的每一次位次更迭,都如同天雷勾动地火,伴随着背后金主世家百亿灵石(市值)的灰飞烟灭或平地起雷。

百晓生精血筑基,发际难存。“世人只看榜单上的排名,却不知这排名背后的残酷。为了那区区 10 点 分数的提升,各大宗门不知烧坏了多少块极品灵石,熬秃了多少位大能的头顶。这哪里是榜单,这分明是用算力和发际线堆出来的‘封神榜’!”


作者后记: 凡尘智道,如露如电。闲时偶作,抛砖引玉。求君一乐,尽在不言中。

–END–

AI-Powered Search: Google’s Transformation vs. Perplexity

TL;DR, Play the podcast (Audio Overview generated by NotebookLM)

  1. Abstract
  2. Google’s AI Transformation: From PageRank to Gemini-Powered Search
    1. The Search Generative Experience (SGE) Revolution
    2. Google’s LLM Arsenal
    3. Technical Architecture Integration
    4. Key Differentiators of Google’s AI Search
  3. Perplexity AI Architecture: The RAG-Powered Search Revolution
    1. Simplified Architecture View
    2. How Perplexity Works: From Query to Answer
    3. Technical Workflow Diagram
  4. The New Search Paradigm: AI-First vs AI-Enhanced Approaches
    1. Google’s Philosophy: “AI-Enhanced Universal Search”
    2. Perplexity’s Philosophy: “AI-Native Conversational Search”
    3. Comprehensive Technology & Business Comparison
  5. The Future of AI-Powered Search: A New Competitive Landscape
    1. Implementation Strategy Battle: Integration vs. Innovation
    2. The Multi-Modal Future
    3. Business Model Evolution Under AI
    4. Technical Architecture Convergence
    5. The Browser and Distribution Channel Wars
  6. Strategic Implications and Future Outlook
    1. Key Strategic Insights
    2. The New Competitive Dynamics
    3. Looking Ahead: Industry Predictions
  7. Recommendations for Stakeholders
  8. Conclusion

Abstract

This blog examines the rapidly evolving landscape of AI-powered search, comparing Google’s recent transformation with its Search Generative Experience (SGE) and Gemini integration against Perplexity AI‘s native AI-first approach. Both companies now leverage large language models, but with fundamentally different architectures and philosophies.

The New Reality: Google has undergone a dramatic transformation from traditional keyword-based search to an AI-driven conversational answer engine. With the integration of Gemini, LaMDA, PaLM, and the rollout of AI Overviews (formerly SGE), Google now synthesizes information from multiple sources into concise, contextual answers—directly competing with Perplexity’s approach.

Key Findings:

  • Convergent Evolution: Both platforms now use LLMs for answer generation, but Google maintains its traditional search infrastructure while Perplexity was built AI-first from the ground up
  • Architecture Philosophy: Google integrates AI capabilities into its existing search ecosystem (hybrid approach), while Perplexity centers everything around RAG and multi-model orchestration (AI-native approach)
  • AI Technology Stack: Google leverages Gemini (multimodal), LaMDA (conversational), and PaLM models, while Perplexity orchestrates external models (GPT, Claude, Gemini, Llama, DeepSeek)
  • User Experience: Google provides AI Overviews alongside traditional search results, while Perplexity delivers answer-first experiences with citations
  • Market Dynamics: The competition has intensified with Google’s AI transformation, making the choice between platforms more about implementation philosophy than fundamental capabilities

This represents a paradigm shift where the question is no longer “traditional vs. AI search” but rather “how to best implement AI-powered search” with different approaches to integration, user experience, and business models.

Keywords: AI Search, RAG, Large Language Models, Search Architecture, Perplexity AI, Google Search, Conversational AI, SGE, Gemini.

Google has undergone one of the most significant transformations in its history, evolving from a traditional link-based search engine to an AI-powered answer engine. This transformation represents a strategic response to the rise of AI-first search platforms and changing user expectations.

The Search Generative Experience (SGE) Revolution

Google’s Search Generative Experience (SGE), now known as AI Overviews, fundamentally changes how search results are presented:

  • AI-Synthesized Answers: Instead of just providing links, Google’s AI generates comprehensive insights, explanations, and summaries from multiple sources
  • Contextual Understanding: Responses consider user context including location, search history, and preferences for personalized results
  • Multi-Step Query Handling: The system can handle complex, conversational queries that require reasoning and synthesis
  • Real-Time Information Grounding: AI overviews are grounded in current, real-time information while maintaining accuracy

Google’s LLM Arsenal

Google has strategically integrated multiple advanced AI models into its search infrastructure:

Gemini: The Multimodal Powerhouse
  • Capabilities: Understands and generates text, images, videos, and audio
  • Search Integration: Enables complex query handling including visual search, reasoning tasks, and detailed information synthesis
  • Multimodal Processing: Handles queries that combine text, images, and other media types
LaMDA: Conversational AI Foundation
  • Purpose: Powers natural, dialogue-like interactions in search
  • Features: Enables follow-up questions and conversational context maintenance
  • Integration: Supports Google’s shift toward conversational search experiences

PaLM: Large-Scale Language Understanding

  • Role: Provides advanced language processing capabilities
  • Applications: Powers complex reasoning, translation (100+ languages), and contextual understanding
  • Scale: Handles extended documents and multimodal inputs

Technical Architecture Integration

Google’s approach differs from AI-first platforms by layering AI capabilities onto existing infrastructure:

  • Hybrid Architecture: Maintains traditional search capabilities while adding AI-powered features
  • Scale Integration: Leverages existing massive infrastructure and data
  • DeepMind Synergy: Strategic integration of DeepMind research into commercial search applications
  • Continuous Learning: ML ranking algorithms and AI models learn from user interactions in real-time
  • Global Reach: AI features deployed across 100+ languages with localized understanding

Perplexity AI Architecture: The RAG-Powered Search Revolution

Perplexity AI represents a fundamental reimagining of search technology, built on three core innovations:

  1. Retrieval-Augmented Generation (RAG): Combines real-time web crawling with large language model capabilities
  2. Multi-Model Orchestration: Leverages multiple AI models (GPT, Claude, Gemini, Llama, DeepSeek) for optimal responses
  3. Integrated Citation System: Provides transparent source attribution with every answer

The platform offers multiple access points to serve different user needs: Web Interface, Mobile App, Comet Browser, and Enterprise API.

Core Architecture Components

Simplified Architecture View

For executive presentations and high-level discussions, this three-layer view highlights the essential components:

How Perplexity Works: From Query to Answer

Understanding Perplexity’s workflow reveals why it delivers fundamentally different results than traditional search engines. Unlike Google’s approach of matching keywords to indexed pages, Perplexity follows a sophisticated multi-step process:

The Eight-Step Journey

  1. Query Reception: User submits a natural language question through any interface
  2. Real-Time Retrieval: Custom crawlers search the web for current, relevant information
  3. Source Indexing: Retrieved content is processed and indexed in real-time
  4. Context Assembly: RAG system compiles relevant information into coherent context
  5. Model Selection: AI orchestrator chooses the optimal model(s) for the specific query type
  6. Answer Generation: Selected model(s) generate comprehensive responses using retrieved context
  7. Citation Integration: System automatically adds proper source attribution
  8. Response Delivery: Final answer with citations is presented to the user

Technical Workflow Diagram

The sequence below shows how a user query flows through Perplexity’s system.

This process typically completes in under 3 seconds, delivering both speed and accuracy.

The New Search Paradigm: AI-First vs AI-Enhanced Approaches

The competition between Google and Perplexity has evolved beyond traditional vs. AI search to represent two distinct philosophies for implementing AI-powered search experiences.

  • Hybrid Integration: Layer advanced AI capabilities onto proven search infrastructure
  • Comprehensive Coverage: Maintain traditional search results alongside AI-generated overviews
  • Gradual Transformation: Evolve existing user behaviors rather than replace them entirely
  • Scale Advantage: Leverage massive existing data and infrastructure for AI training and deployment
  • Model Agnostic: Orchestrate best-in-class models rather than developing proprietary AI
  • Clean Slate Design: Built from the ground up with AI-first architecture
  • Answer-Centric: Focus entirely on direct answer generation with source attribution
  • Conversational Flow: Design for multi-turn, contextual conversations rather than single queries

Comprehensive Technology & Business Comparison

DimensionGoogle AI-Enhanced SearchPerplexity AI-Native Search
InputNatural language + traditional keywordsPure natural language, conversational
AI ModelsGemini, LaMDA, PaLM (proprietary)GPT, Claude, Gemini, Llama, DeepSeek (orchestrated)
ArchitectureHybrid (AI + traditional infrastructure)Pure AI-first (RAG-centered)
RetrievalEnhanced index + Knowledge Graph + real-timeCustom crawler + real-time retrieval
Core TechAI Overviews + traditional rankingRAG + multi-model orchestration
OutputHybrid (AI Overview + links + ads)Direct answers with citations
ContextLimited conversational memoryFull multi-turn conversation memory
ExtensionsMaps, News, Shopping, Ads integrationDocument search, e-commerce, APIs
BusinessAd-driven + AI premium featuresSubscription + API + e-commerce
UX“AI answers + traditional options”“Conversational AI assistant”
ProductsGoogle Search with SGE/AI OverviewPerplexity Web/App, Comet Browser
DeploymentGlobal rollout with localizationGlobal expansion, English-focused
Data AdvantageMassive proprietary data + real-timeReal-time web data + model diversity
ProductsGoogle Search, AdsPerplexity Web/App, Comet Browser

The Future of AI-Powered Search: A New Competitive Landscape

The integration of AI into search has fundamentally changed the competitive landscape. Rather than a battle between traditional and AI search, we now see different approaches to implementing AI-powered experiences competing for user mindshare and market position.

Implementation Strategy Battle: Integration vs. Innovation

Google’s Integration Strategy:

  • Advantage: Massive user base and infrastructure to deploy AI features at scale
  • Challenge: Balancing AI innovation with existing business model dependencies
  • Approach: Gradual rollout of AI features while maintaining traditional search options

Perplexity’s Innovation Strategy:

  • Advantage: Clean slate design optimized for AI-first experiences
  • Challenge: Building user base and competing with established platforms
  • Approach: Focus on superior AI experience to drive user acquisition

The Multi-Modal Future

Both platforms are moving toward comprehensive multi-modal experiences:

  • Visual Search Integration: Google Lens vs. Perplexity’s image understanding capabilities
  • Voice-First Interactions: Google Assistant integration vs. conversational AI interfaces
  • Video and Audio Processing: Gemini’s multimodal capabilities vs. orchestrated model approaches
  • Document Intelligence: Enterprise document search and analysis capabilities

Business Model Evolution Under AI

Advertising Model Transformation:

  • Google must adapt its ad-centric model to AI Overviews without disrupting user experience
  • Challenge of monetizing direct answers vs. traditional click-through advertising
  • Need for new ad formats that work with conversational AI

Subscription and API Models:

  • Perplexity’s success with subscription tiers validates alternative monetization
  • Growing enterprise demand for AI-powered search APIs and integrations
  • Premium features becoming differentiators (document search, advanced models, higher usage limits)

Technical Architecture Convergence

Despite different starting points, both platforms are converging on similar technical capabilities:

  • Real-Time Information: Both now emphasize current, up-to-date information retrieval
  • Source Attribution: Transparency and citation becoming standard expectations
  • Conversational Context: Multi-turn conversation support across platforms
  • Model Diversity: Google developing multiple specialized models, Perplexity orchestrating external models

The Browser and Distribution Channel Wars

Perplexity’s Chrome Acquisition Strategy:

  • $34.5B all-cash bid for Chrome represents unprecedented ambition in AI search competition
  • Strategic Value: Control over browser defaults, user data, and search distribution
  • Market Impact: Success would fundamentally alter competitive dynamics and user acquisition costs
  • Regulatory Reality: Bid likely serves as strategic positioning and leverage rather than realistic acquisition

Alternative Distribution Strategies:

  • AI-native browsers (Comet) as specialized entry points
  • API integrations into enterprise and developer workflows
  • Mobile-first experiences capturing younger user demographics

Strategic Implications and Future Outlook

The competition between Google’s AI-enhanced approach and Perplexity’s AI-native strategy represents a fascinating case study in how established platforms and startups approach technological transformation differently.

Key Strategic Insights

  • The AI Integration Challenge: Google’s transformation demonstrates that even dominant platforms must fundamentally reimagine their core products to stay competitive in the AI era
  • Architecture Philosophy Matters: The choice between hybrid integration (Google) vs. AI-first design (Perplexity) creates different strengths, limitations, and user experiences
  • Business Model Pressure: AI-powered search challenges traditional advertising models, forcing experimentation with subscriptions, APIs, and premium features
  • User Behavior Evolution: Both platforms are driving the shift from “search and browse” to “ask and receive” interactions, fundamentally changing how users access information

The New Competitive Dynamics

Advantages of Google’s AI-Enhanced Approach:

  • Massive scale and infrastructure for global AI deployment
  • Existing user base to gradually transition to AI features
  • Deep integration with knowledge graphs and proprietary data
  • Ability to maintain traditional search alongside AI innovations

Advantages of Perplexity’s AI-Native Approach:

  • Optimized user experience designed specifically for conversational AI
  • Agility to implement cutting-edge AI techniques without legacy constraints
  • Model-agnostic architecture leveraging best-in-class external AI models
  • Clear value proposition for users seeking direct, cited answers

Looking Ahead: Industry Predictions

Near-Term (1-2 years):

  • Continued convergence of features between platforms
  • Google’s global rollout of AI Overviews across all markets and languages
  • Perplexity’s expansion into enterprise and specialized vertical markets
  • Emergence of more AI-native search platforms following Perplexity’s model

Medium-Term (3-5 years):

  • AI-powered search becomes the standard expectation across all platforms
  • Specialized AI search tools for professional domains (legal, medical, scientific research)
  • Integration of real-time multimodal capabilities (live video analysis, augmented reality search)
  • New regulatory frameworks for AI-powered information systems

Long-Term (5+ years):

  • Fully conversational AI assistants replace traditional search interfaces
  • Personal AI agents that understand individual context and preferences
  • Integration with IoT and ambient computing for seamless information access
  • Potential emergence of decentralized, blockchain-based search alternatives

Recommendations for Stakeholders

For Technology Leaders:

  • Hybrid Strategy: Consider Google’s approach of enhancing existing systems with AI rather than complete rebuilds
  • Model Orchestration: Investigate Perplexity’s approach of orchestrating multiple AI models for optimal results
  • Real-Time Capabilities: Invest in real-time information retrieval and processing systems
  • Citation Systems: Implement transparent source attribution to build user trust

For Business Strategists:

  • Revenue Model Innovation: Experiment with subscription, API, and premium feature models beyond traditional advertising
  • User Experience Focus: Prioritize conversational, answer-first experiences in product development
  • Distribution Strategy: Evaluate the importance of browser control and default search positions
  • Competitive Positioning: Decide between AI-enhancement of existing products vs. AI-native alternatives

For Investors:

  • Platform Risk Assessment: Evaluate how established platforms are adapting to AI disruption
  • Technology Differentiation: Assess the sustainability of competitive advantages in rapidly evolving AI landscape
  • Business Model Viability: Monitor the success of alternative monetization strategies beyond advertising
  • Regulatory Impact: Consider potential regulatory responses to AI-powered information systems and search market concentration

The future of search will be determined by execution quality, user adoption, and the ability to balance innovation with practical business considerations. Both Google and Perplexity have established viable but different paths forward, setting the stage for continued innovation and competition in the AI-powered search landscape.

  • Monitor the browser control battle and distribution channel acquisitions
  • Technology Differentiation: Assess the sustainability of competitive advantages in rapidly evolving AI landscape
  • Business Model Viability: Monitor the success of alternative monetization strategies beyond advertising
  • Regulatory Impact: Consider potential regulatory responses to AI-powered information systems and search market concentration

Conclusion

The evolution of search from Google’s traditional PageRank-driven approach to today’s AI-powered landscape represents one of the most significant technological shifts in internet history. Google’s recent transformation with its Search Generative Experience and Gemini integration demonstrates that even the most successful platforms must reinvent themselves to remain competitive in the AI era.

The competition between Google’s AI-enhanced strategy and Perplexity’s AI-native approach offers valuable insights into different paths for implementing AI at scale. Google’s hybrid approach leverages massive existing infrastructure while gradually transforming user experiences, while Perplexity’s clean-slate design optimizes entirely for conversational AI interactions.

As both platforms continue to evolve, the ultimate winners will be users who gain access to more intelligent, efficient, and helpful ways to access information. The future of search will likely feature elements of both approaches: the scale and comprehensiveness of Google’s enhanced platform combined with the conversational fluency and transparency of AI-native solutions.

The battle for search supremacy in the AI era has only just begun, and the innovations emerging from this competition will shape how humanity accesses and interacts with information for decades to come.


This analysis reflects the state of AI-powered search as of August 2025. The rapidly evolving nature of AI technology and competitive dynamics may significantly impact future developments. Both Google and Perplexity continue to innovate at unprecedented pace, making ongoing monitoring essential for stakeholders in this space. This analysis represents the current state of AI-powered search as of August 2025. The rapidly evolving nature of AI technology and competitive landscape may impact future developments.

Prompt Engineering for LLM

2024-Feb-04: 1st Version

  1. Introduction
  2. Basic Prompting
    1. Zero-shot
    2. Few-shot
    3. Hallucination
  3. Perfect Prompt Formula for ChatBots
  4. RAG, CoT, ReACT, SASE, DSP …
    1. RAG: Retrieval-Augmented Generation
    2. CoT: Chain-of-Thought
    3. Self-Ask + Search Engine
    4. ReAct: Reasoning and Acting
    5. DSP: Directional Stimulus Prompting
  5. Summary and Conclusion
  6. Reference
Prompt engineering is like adjusting audio without opening the equipment.

Introduction

Prompt Engineering, also known as In-Context Prompting, refers to methods for communicating with a Large Language Model (LLM) like GPT (Generative Pre-trained Transformer) to manipulate/steer its behaviour for expected outcomes without updating, retraining or fine-tuning the model weights. 

Researchers, developers, or users may engage in prompt engineering to instruct a model for specific tasks, improve the model’s performance, or adapt it to better understand and respond to particular inputs. It is an empirical science and the effect of prompt engineering methods can vary a lot among models, thus requiring heavy experimentation and heuristics.

This post only focuses on prompt engineering for autoregressive language models, so nothing with image generation or multimodality models.

Basic Prompting

Zero-shot and few-shot learning are the two most basic approaches for prompting the model, pioneered by many LLM papers and commonly used for benchmarking LLM performance. That is to say, Zero-shot and few-shot testing are scenarios used to evaluate the performance of large language models (LLMs) in handling tasks with little or no training data. Here are examples for both:

Zero-shot

Zero-shot learning simply feeds the task text to the model and asks for results.

Scenario: Text Completion (Please try the following input in ChatGPT or Google Bard)

Input:

Task: Complete the following sentence:

Input: The capital of France is ____________.

Output (ChatGPT / Bard):

Output: The capital of France is Paris.

Few-shot

Few-shot learning presents a set of high-quality demonstrations, each consisting of both input and desired output, on the target task. As the model first sees good examples, it can better understand human intention and criteria for what kinds of answers are wanted. Therefore, few-shot learning often leads to better performance than zero-shot. However, it comes at the cost of more token consumption and may hit the context length limit when the input and output text are long.

Scenario: Text Classification

Input:

Task: Classify movie reviews as positive or negative.

Examples:
Review 1: This movie was amazing! The acting was superb.
Sentiment: Positive
Review 2: I couldn't stand this film. The plot was confusing.
Sentiment: Negative

Question:
Review: I'll bet the video game is a lot more fun than the film.
Sentiment:____

Output

Sentiment: Negative

Many studies have explored the construction of in-context examples to maximize performance. They observed that the choice of prompt format, training examples, and the order of the examples can significantly impact performance, ranging from near-random guesses to near-state-of-the-art performance.

Hallucination

In the context of Large Language Models (LLMs), hallucination refers to a situation where the model generates outputs that are incorrect or not grounded in reality. A hallucination occurs when the model produces information that seems plausible or coherent but is actually not accurate or supported by the input data.

For example, in a language generation task, if a model is asked to provide information about a topic and it generates details that are not factually correct or have no basis in the training data, it can be considered as hallucination. This phenomenon is a concern in natural language processing because it can lead to the generation of misleading or false information.

Addressing hallucination in LLMs is a challenging task, and researchers are actively working on developing methods to improve the models’ accuracy and reliability. Techniques such as fine-tuning, prompt engineering, and designing more specific evaluation metrics are among the approaches used to mitigate hallucination in language models.

Perfect Prompt Formula for ChatBots

For personal daily documenting work such as text generation, there are six key components making up the perfect formula for ChatGPT and Google Bard:

Task, Context, Exemplars, Persona, Format, and Tone.

Prompt Formula for ChatBots
  1. The Task sentence needs to articulate the end goal and start with an action verb.
  2. Use three guiding questions to help structure relevant and sufficient Context.
  3. Exemplars can drastically improve the quality of the output by giving specific examples for the AI to reference.
  4. For Persona, think of who you would ideally want the AI to be in the given task situation.
  5. Visualizing your desired end result will let you know what format to use in your prompt.
  6. And you can actually use ChatGPT to generate a list of Tone keywords for you to use!
Example from Jeff Su: Master the Perfect ChatGPT Prompt Formula 

RAG, CoT, ReACT, SASE, DSP …

If you are ever curious about what the heck are those techies talking about with the above words? Please continues …

OK, so here’s the deal. We’re diving into the world of academia, talking about machine learning and large language models in the computer science and engineering domains. I’ll try to explain it in a simple way, but you can always dig deeper into these topics elsewhere.

RAG: Retrieval-Augmented Generation

RAG (Retrieval-Augmented Generation): RAG typically refers to a model that combines both retrieval and generation approaches. It might use a retrieval mechanism to retrieve relevant information from a database or knowledge base and then generate a response based on that retrieved information. In real applications, the users’ input and the model’s output will be pre/post-processed to follow certain rules and obey laws and regulations.

RAG: Retrieval-Augmented Generation

Here is a simplified example of using a Retrieval-Augmented Generation (RAG) model for a question-answering task. In this example, we’ll use a system that retrieves relevant passages from a knowledge base and generates an answer based on that retrieved information.

Input:

User Query: What are the symptoms of COVID-19?

Knowledge Base:

1. Title: Symptoms of COVID-19
Content: COVID-19 symptoms include fever, cough, shortness of breath, fatigue, body aches, loss of taste or smell, sore throat, etc.

2. Title: Prevention measures for COVID-19
Content: To prevent the spread of COVID-19, it's important to wash hands regularly, wear masks, practice social distancing, and get vaccinated.

3. Title: COVID-19 Treatment
Content: COVID-19 treatment involves rest, hydration, and in severe cases, hospitalization may be required.

RAG Model Output:

Generated Answer: 

The symptoms of COVID-19 include fever, cough, shortness of breath, fatigue, body aches, etc.

Remark: ChatGPT 3.5 will give basic results like the above. But, Google Bard will provide extra resources like CDC links and other sources it gets from the Search Engines. We could guess Google used a different framework to OpenAI.

CoT: Chain-of-Thought

Chain-of-thought (CoT) prompting (Wei et al. 2022) generates a sequence of short sentences to describe reasoning logics step by step, known as reasoning chains or rationales, to eventually lead to the final answer.

The benefit of CoT is more pronounced for complicated reasoning tasks while using large models (e.g. with more than 50B parameters). Simple tasks only benefit slightly from CoT prompting.

Tree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, essentially creating a tree structure. The search process can be BFS or DFS while each state is evaluated by a classifier (via a prompt) or majority vote.

CoT : Chain-of-Thought and ToT: Tree-of-Thought

Self-Ask + Search Engine

Self-Ask (Press et al. 2022) is a method to repeatedly prompt the model to ask follow-up questions to construct the thought process iteratively. Follow-up questions can be answered by search engine results.

Self-Ask+Search Engine Example

ReAct: Reasoning and Acting

ReAct (Reason + Act; Yao et al. 2023) combines iterative CoT prompting with queries to Wikipedia APIs to search for relevant entities and content and then add it back into the context.

In each trajectory consists of multiple thought-action-observation steps (i.e. dense thought), where free-form thoughts are used for various purposes.

Example of ReAct from pp18.(Reason + Act; Yao et al. 2023)
ReAct: Reasoning and Acting

Specifically, from the paper, the authors use a combination of thoughts that decompose questions (“I need to search x, find y, then find z”), extract information from Wikipedia observations (“x was started in 1844”, “The paragraph does not tell x”), perform commonsense (“x is not y, so z must instead be…”) or arithmetic reasoning (“1844 < 1989”), guide search reformulation (“maybe I can search/lookup x instead”), and synthesize the final answer (“…so the answer is x”).

DSP: Directional Stimulus Prompting

Directional Stimulus Prompting (DSP, Z. Li 2023), is a novel framework for guiding black-box large language models (LLMs) toward specific desired outputs.  Instead of directly adjusting LLMs, this method employs a small tunable policy model to generate an auxiliary directional stimulus (hints) prompt for each input instance. 

DSP: Directional Stimulus Prompting

Summary and Conclusion

Prompt engineering involves carefully crafting these prompts to achieve desired results. It can include experimenting with different phrasings, structures, and strategies to elicit the desired information or responses from the model. This process is crucial because the performance of language models can be sensitive to how prompts are formulated.

I believe a lot of researchers will agree with me. Some prompt engineering papers don’t need to be 8 pages long. They could explain the important points in just a few lines and use the rest for benchmarking. 

As researchers and developers delve further into the realms of prompt engineering, they continue to push the boundaries of what these sophisticated models can achieve.

To achieve this, it’s important to create a user-friendly LLM benchmarking system that many people will use. Developing better methods for creating prompts will help advance language models and improve how we use LLMs. These efforts will have a big impact on natural language processing and related fields.

Reference

  1. Weng, Lilian. (Mar 2023). Prompt Engineering. Lil’Log.
  2. IBM (Jan 2024) 4 Methods of Prompt Engineering
  3. Jeff Su (Aug 2023) Master the Perfect ChatGPT Prompt Formula

Google Cloud Professional Machine Learning Engineer Exam Prep Guide and Study Tips

Content created by the author and reviewed by GPT.

Obtaining the Google Cloud Professional Machine Learning Engineer (MLE) certification is a remarkable achievement for those interested in a machine learning career. As someone who recently passed the exam, I’m here to share helpful tips and insights about the journey. Whether you’re considering taking the exam or currently preparing for it, I hope this guide will help you with valuable information based on my experience.

Three Steps in Preparation

Step 1: Read the Exam Guide Thoroughly

Before diving into your exam preparation, start by carefully reading the official Exam Guide provided by Google. This document is the roadmap for us to understand the key topics and expectations for the certification.

It’s essential to have a clear grasp of what the exam covers before we begin our study journey. Revisit the ML basics via Google’s Crash Course to clarify the details.

Step 2: Learn Best Practices for Implementing ML on Google Cloud

Machine learning is a dynamic field with various approaches and techniques. Google provides best practices for implementing ML solutions on their platform, and this practical knowledge is invaluable. Learning these best practices will not only help us in the exam but also equip us with the skills necessary for real-world ML projects.

Official Google documents, which include keywords such as best practice, machine learning solution, and data pipeline, are all worth reading.

Step 3: Consult the ExamTopic Website

The ExamTopic website is a valuable resource for exam preparation. However, it’s essential to use it strategically. This resource is not a “cheat sheet” or a “shortcut” to the exam, so save it for later, like after we’ve refreshed our knowledge through reading the official documentation and best practices.

While ExamTopic can provide insights into potential exam questions, remember that there are no official answers. The answers offered on the web and those voted ones by users may not be correct.

Get Ready for Exam and Study Tips
  1. Exam Online or Onsite
    • There are two ways to take the exam: Online and Onsite. If you choose the online option, make sure your home WIFI is stable and your system is checked (webcam, microphone, Secure Browser).
    • You will be asked to adjust your device’s security settings, such as turning off the Firewall or enabling screen sharing. If you’re not comfortable making these changes, consider booking an Onsite Exam.
    • If any issues arise during the exam, don’t panic! Just contact Kryterion support team through Live Chat. They can help with things like reopening the launch button for you or adjusting the time.
    • The key is to stay calm and reach out for help if needed to ensure a smooth exam experience!
  2. Reading vs. Watching
    • In the age of abundant online resources, it’s tempting to jump straight into video tutorials and courses. However, for the best retention of knowledge, start by actively reading Google’s documentation.
    • Passive learning through watching videos may lead to omitted details. Reading engages your mind and helps you absorb information effectively.
  3. Understand Trade-offs
    • Machine learning involves making critical decisions, such as balancing speed and accuracy. Take the time to understand the trade-offs involved in various ML solutions. This understanding will prove invaluable not only in the exam but also in real-world ML projects.
  4. Reading Comprehension
    • During the exam, we will encounter questions that provide background information on a problem, stakeholder expectations, and resource limitations. Treat these questions like reading comprehension exercises, as key details hidden within can guide us to the correct answer. Pay close attention to keywords that may hold the solution.
  5. Time Management
    • The exam requires answering 60 questions within a limited timeframe like 2 hours, which may vary in the future. Manage our time wisely by marking questions we’re unsure about for review later.
    • Prioritize the questions we can confidently answer first and revisit the marked ones before submitting our exam in the end.
  6. Stress Management
    • Even if you tell yourself not to stress, it’s natural to feel some pressure during the exam.
    • Consider conducting simulated practice exams to strengthen your nerves, especially in the case that you haven’t taken any exam for a long time. This practice can help improve your mental preparedness for the actual exam.

In the end, I wish you the best of luck in your journey towards achieving the Google Cloud Professional Machine Learning Engineer certification. Remember that diligent preparation, careful reading, and a strategic approach to resources can significantly enhance your chances of success.

Stay confident, stay focused, and may you pass the exam as soon as possible!

-END-

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