Yapay zeka d\u00fcnyas\u0131, \u00f6zellikle b\u00fcy\u00fck dil modelleri (LLM’ler) ve GPT benzeri sistemlerin y\u00fckseli\u015fiyle birlikte, ak\u0131l y\u00fcr\u00fctme ve problem \u00e7\u00f6zme yetenekleri konusunda devrim niteli\u011finde ilerlemeler kaydetti. Ancak bu sistemlerin “d\u00fc\u015f\u00fcnme” veya “ara\u015ft\u0131rma” bi\u00e7imleri tek tip de\u011fildir. Bu makalede, s\u0131\u011f (shallow) ve derin (deep) ajan yakla\u015f\u0131mlar\u0131n\u0131 inceleyerek, GPT benzeri sistemlerin i\u00e7 dinamiklerinde derinlemesine ara\u015ft\u0131rman\u0131n nas\u0131l i\u015fledi\u011fini, hangi mekanizmalarla bu karma\u015f\u0131k s\u00fcre\u00e7leri y\u00fcr\u00fctt\u00fc\u011f\u00fcn\u00fc ve bu iki yakla\u015f\u0131m\u0131n avantajlar\u0131n\u0131, dezavantajlar\u0131n\u0131 ve uygulama alanlar\u0131n\u0131 detayl\u0131 bir \u015fekilde ele alaca\u011f\u0131z.<\/p>\n
Yapay zeka ba\u011flam\u0131nda bir “ajan”, \u00e7evresini alg\u0131layan ve hedeflerine ula\u015fmak i\u00e7in eylemlerde bulunan otonom bir varl\u0131kt\u0131r. GPT benzeri sistemler de metin tabanl\u0131 bir ortamda hareket eden, bilgi \u00fcreten ve i\u015fleyen ajanlar olarak d\u00fc\u015f\u00fcn\u00fclebilir. Bu ajanlar\u0131n ak\u0131l y\u00fcr\u00fctme kapasiteleri, basit bir sorguya do\u011frudan yan\u0131t vermekten, karma\u015f\u0131k bir problemi birden fazla ad\u0131mda \u00e7\u00f6zmeye kadar farkl\u0131 derinliklerde olabilir.<\/p>\n
Bir LLM, ald\u0131\u011f\u0131 girdiyi (prompt) i\u015fleyerek en olas\u0131 \u00e7\u0131kt\u0131 dizisini \u00fcreten bir sistemdir. Bu s\u00fcre\u00e7, temelde bir sonraki kelimeyi tahmin etmeye dayan\u0131r. Ancak modern LLM’ler, bu basit mekanizmay\u0131 kullanarak \u00e7ok daha karma\u015f\u0131k g\u00f6revleri yerine getirebilirler. Bir LLM ajan\u0131, genellikle bir hedefi ger\u00e7ekle\u015ftirmek i\u00e7in plan yapar, eylemler \u00fcretir, bu eylemlerin sonu\u00e7lar\u0131n\u0131 g\u00f6zlemler ve gerekti\u011finde plan\u0131n\u0131 revize eder.<\/p>\n
Ak\u0131l y\u00fcr\u00fctmede derinlik, bir g\u00f6revi tamamlamak i\u00e7in gereken d\u00fc\u015f\u00fcnsel ad\u0131mlar\u0131n say\u0131s\u0131n\u0131 ve karma\u015f\u0131kl\u0131\u011f\u0131n\u0131 ifade eder. S\u0131\u011f ak\u0131l y\u00fcr\u00fctme, genellikle tek bir ad\u0131mda veya \u00e7ok az ad\u0131mda sonuca ula\u015f\u0131rken, derin ak\u0131l y\u00fcr\u00fctme, bir dizi mant\u0131ksal \u00e7\u0131kar\u0131m, ara ad\u0131m, do\u011frulama ve hatta kendi kendini d\u00fczeltme mekanizmalar\u0131 gerektirir. Bu derinlik, modelin i\u00e7sel bilgi taban\u0131n\u0131 ne kadar etkili kulland\u0131\u011f\u0131 ve harici ara\u00e7larla ne kadar entegre olabildi\u011fi ile de do\u011frudan ili\u015fkilidir.<\/p>\n
S\u0131\u011f ajanlar, genellikle do\u011frudan bir soruya an\u0131nda yan\u0131t veren veya basit bir talimat\u0131 tek seferde yerine getiren sistemlerdir. Bu t\u00fcr ajanlar, h\u0131zl\u0131 ve verimli olmalar\u0131 gereken durumlar i\u00e7in idealdir, ancak karma\u015f\u0131k veya \u00e7ok ad\u0131ml\u0131 problemler kar\u015f\u0131s\u0131nda yetersiz kalabilirler.<\/p>\n
S\u0131\u011f bir ajan, ald\u0131\u011f\u0131 bir sorguyu do\u011frudan kendi e\u011fitim verisinden veya mevcut bilgi taban\u0131ndan e\u015fle\u015ftirerek yan\u0131tlar. \u00d6rne\u011fin, “T\u00fcrkiye’nin ba\u015fkenti neresidir?” gibi bir soruya an\u0131nda “Ankara” cevab\u0131n\u0131 verebilir. Bu, modelin do\u011frudan bilgi \u00e7ekimi (retrieval) yetene\u011fine dayan\u0131r ve genellikle ek bir d\u00fc\u015f\u00fcnme veya planlama ad\u0131m\u0131 gerektirmez.<\/p>\n
\nPrompt: \"Mars'\u0131n y\u00fczey s\u0131cakl\u0131\u011f\u0131 ortalama ka\u00e7 derecedir?\"\nOutput: \"Mars'\u0131n ortalama y\u00fczey s\u0131cakl\u0131\u011f\u0131 yakla\u015f\u0131k -63 santigrat derecedir.\"\n<\/pre>\n<\/code><\/p>\n
S\u0131\u011f Ak\u0131l Y\u00fcr\u00fctmenin S\u0131n\u0131rlar\u0131<\/h4>\n
S\u0131\u011f ajanlar, yarat\u0131c\u0131l\u0131k, karma\u015f\u0131k problem \u00e7\u00f6zme, \u00e7ok ad\u0131ml\u0131 planlama veya kendi hatalar\u0131n\u0131 fark edip d\u00fczeltme gibi yeteneklerden yoksundur. Bir soru birden fazla bilgi par\u00e7as\u0131n\u0131n birle\u015ftirilmesini veya mant\u0131ksal \u00e7\u0131kar\u0131m yap\u0131lmas\u0131n\u0131 gerektirdi\u011finde, s\u0131\u011f bir ajan genellikle yetersiz veya yanl\u0131\u015f yan\u0131tlar \u00fcretebilir. Bu durum, \"hallucination\" olarak bilinen yan\u0131lsamalara da yol a\u00e7abilir.<\/p>\n
Ne Zaman Yeterlidir?<\/h4>\n
S\u0131\u011f yakla\u015f\u0131mlar, bilgi alma, k\u0131sa metin \u00f6zetleme, basit \u00e7eviriler veya h\u0131zl\u0131 soru-cevap sistemleri gibi g\u00f6revler i\u00e7in son derece yeterli ve maliyet-etkin olabilir. Kullan\u0131c\u0131lar\u0131n an\u0131nda ve kesin bilgiye ihtiya\u00e7 duydu\u011fu durumlarda tercih edilirler.<\/p>\n
Derin Ajanlar: \u00c7ok Ad\u0131ml\u0131 Ak\u0131l Y\u00fcr\u00fctme ve Karma\u015f\u0131k Problem \u00c7\u00f6zme<\/h3>\n
Derin ajanlar, karma\u015f\u0131k problemleri \u00e7\u00f6zmek i\u00e7in birden fazla ad\u0131mdan olu\u015fan bir ak\u0131l y\u00fcr\u00fctme s\u00fcreci izlerler. Bu s\u00fcre\u00e7, planlama, ara ad\u0131mlar olu\u015fturma, sonu\u00e7lar\u0131 de\u011ferlendirme ve gerekti\u011finde d\u00fczeltme yapmay\u0131 i\u00e7erebilir. Bu t\u00fcr ajanlar, daha g\u00fcvenilir ve do\u011fru sonu\u00e7lar \u00fcretme potansiyeline sahiptir.<\/p>\n
Zincirleme D\u00fc\u015f\u00fcnce (Chain-of-Thought - CoT)<\/h4>\n
CoT, LLM'lerin karma\u015f\u0131k sorular\u0131 ad\u0131m ad\u0131m \u00e7\u00f6zmesini sa\u011flayan bir prompt m\u00fchendisli\u011fi tekni\u011fidir. Modelden sadece nihai cevab\u0131 de\u011fil, ayn\u0131 zamanda bu cevaba nas\u0131l ula\u015ft\u0131\u011f\u0131n\u0131 g\u00f6steren ara ad\u0131mlar\u0131 da a\u00e7\u0131klamas\u0131n\u0131 isteriz. Bu, modelin i\u00e7sel \"d\u00fc\u015f\u00fcnce\" s\u00fcrecini d\u0131\u015fa vurarak, daha mant\u0131kl\u0131 ve tutarl\u0131 \u00e7\u0131kt\u0131lar \u00fcretmesini sa\u011flar.<\/p>\n
\nPrompt: \"Bir matematik problemini \u00e7\u00f6z: Ay\u015fe'nin 5 elmas\u0131 var, Mehmet Ay\u015fe'ye 3 elma daha verdi. Sonra Ay\u015fe 2 elma yedi. Ay\u015fe'nin ka\u00e7 elmas\u0131 kald\u0131? Ad\u0131m ad\u0131m d\u00fc\u015f\u00fcn.\"\nOutput:\n\"Ad\u0131m 1: Ay\u015fe'nin ba\u015flang\u0131\u00e7ta 5 elmas\u0131 vard\u0131.\nAd\u0131m 2: Mehmet 3 elma verdi, yani 5 + 3 = 8 elmas\u0131 oldu.\nAd\u0131m 3: Ay\u015fe 2 elma yedi, yani 8 - 2 = 6 elmas\u0131 kald\u0131.\nSonu\u00e7: Ay\u015fe'nin 6 elmas\u0131 kald\u0131.\"\n<\/pre>\n<\/code><\/p>\n
D\u00fc\u015f\u00fcnce A\u011fac\u0131 (Tree-of-Thought - ToT) ve Yans\u0131ma (Reflection)<\/h4>\n
ToT, CoT'nin daha geli\u015fmi\u015f bir versiyonudur. Burada model, birden fazla olas\u0131 d\u00fc\u015f\u00fcnce yolunu ke\u015ffeder, her bir yolu de\u011ferlendirir ve en umut verici olan\u0131 se\u00e7er. Yans\u0131ma (Reflection) ise, modelin kendi \u00fcretti\u011fi \u00e7\u0131kt\u0131lar\u0131 veya ara ad\u0131mlar\u0131 ele\u015ftirel bir \u015fekilde incelemesini ve potansiyel hatalar\u0131 veya iyile\u015ftirme alanlar\u0131n\u0131 belirlemesini sa\u011flar. Bu, modelin kendi kendini d\u00fczeltebilme yetene\u011fini art\u0131r\u0131r.<\/p>\n
Ara\u00e7 Kullan\u0131m\u0131 (Tool Use) ve Harici Entegrasyonlar<\/h4>\n
Derin ajanlar, sadece kendi i\u00e7sel bilgilerine dayanmakla kalmaz, ayn\u0131 zamanda harici ara\u00e7lar\u0131 (\u00f6rne\u011fin, hesap makineleri, web arama motorlar\u0131, veritabanlar\u0131 veya API'ler) kullanma yetene\u011fine de sahip olabilirler. Bu, modelin bilgi eksikliklerini gidermesini, g\u00fcncel verilere eri\u015fmesini ve daha do\u011fru, ger\u00e7ek zamanl\u0131 sonu\u00e7lar \u00fcretmesini sa\u011flar. Bu, LLM'lerin yeteneklerini \u00f6nemli \u00f6l\u00e7\u00fcde geni\u015fletir.<\/p>\n
GPT Benzeri Sistemlerde Derin Ara\u015ft\u0131rman\u0131n Mekanikleri<\/h3>\n
GPT benzeri sistemlerin derin ara\u015ft\u0131rma yapabilme yetene\u011fi, sadece prompt m\u00fchendisli\u011fi teknikleriyle de\u011fil, ayn\u0131 zamanda temel model mimarisi ve e\u011fitim s\u00fcre\u00e7leriyle de yak\u0131ndan ili\u015fkilidir.<\/p>\n
\u0130\u00e7selle\u015ftirilmi\u015f Bilgi ve Model Mimarisi<\/h4>\n
Transformer mimarisi \u00fczerine kurulu LLM'ler, milyarlarca parametre arac\u0131l\u0131\u011f\u0131yla devasa metin veri k\u00fcmelerinden \u00f6\u011frenirler. Bu e\u011fitim s\u00fcreci, modelin sadece kelimeler aras\u0131ndaki istatistiksel ili\u015fkileri de\u011fil, ayn\u0131 zamanda karma\u015f\u0131k kavramsal ili\u015fkileri, d\u00fcnya bilgisini ve hatta bir dereceye kadar mant\u0131ksal \u00e7\u0131kar\u0131m yeteneklerini de i\u00e7selle\u015ftirmesini sa\u011flar. Derin ara\u015ft\u0131rma, bu i\u00e7selle\u015ftirilmi\u015f bilginin farkl\u0131 katmanlar\u0131 aras\u0131nda dinamik ba\u011flant\u0131lar kurarak ger\u00e7ekle\u015fir.<\/p>\n
Prompt M\u00fchendisli\u011finin Rol\u00fc<\/h4>\n
Prompt m\u00fchendisli\u011fi, derin ara\u015ft\u0131rmay\u0131 tetikleyen anahtard\u0131r. Do\u011fru yap\u0131land\u0131r\u0131lm\u0131\u015f bir prompt, modelin i\u00e7sel ak\u0131l y\u00fcr\u00fctme yeteneklerini ortaya \u00e7\u0131kar\u0131r. \"Ad\u0131m ad\u0131m d\u00fc\u015f\u00fcn\", \"bir plan olu\u015ftur\", \"farkl\u0131 se\u00e7enekleri de\u011ferlendir\" gibi ifadeler, modelin daha derin bir i\u015flem moduna ge\u00e7mesini sa\u011flar. Bu, modelin i\u00e7sel olarak daha fazla \"token\" \u00fcreterek, nihai cevaba ula\u015fmadan \u00f6nce bir dizi ara durum veya d\u00fc\u015f\u00fcnce ad\u0131m\u0131 sim\u00fcle etmesine olanak tan\u0131r.<\/p>\n
\u00c7\u0131kar\u0131msal Ad\u0131mlar\u0131n Sim\u00fclasyonu<\/h4>\n
LLM'ler, her bir token'\u0131 tahmin ederken, asl\u0131nda potansiyel bir \"d\u00fc\u015f\u00fcnce\" ad\u0131m\u0131n\u0131 sim\u00fcle ederler. Derin ak\u0131l y\u00fcr\u00fctme, bu sim\u00fclasyonlar\u0131n zincirleme veya a\u011fa\u00e7 yap\u0131s\u0131nda birbiri \u00fczerine in\u015fa edilmesiyle ger\u00e7ekle\u015fir. Model, bir ara ad\u0131m \u00fcretti\u011finde, bu ara ad\u0131m bir sonraki token tahmininin girdisi haline gelir ve b\u00f6ylece ak\u0131l y\u00fcr\u00fctme s\u00fcreci ilerler. Bu, bir t\u00fcr \"i\u00e7sel monolog\" veya \"self-talk\" olarak d\u00fc\u015f\u00fcn\u00fclebilir.<\/p>\n
Derin Ara\u015ft\u0131rma Stratejileri ve Prompt M\u00fchendisli\u011fi<\/h3>\n
Derin ara\u015ft\u0131rma yeteneklerini etkin bir \u015fekilde kullanmak i\u00e7in \u00e7e\u015fitli prompt m\u00fchendisli\u011fi stratejileri geli\u015ftirilmi\u015ftir.<\/p>\n
Ad\u0131m Ad\u0131m Talimatland\u0131rma ve A\u015famal\u0131 \u00c7\u00f6z\u00fcmleme<\/h4>\n
Modelden bir g\u00f6revi do\u011frudan \u00e7\u00f6zmesini istemek yerine, g\u00f6revi daha k\u00fc\u00e7\u00fck, y\u00f6netilebilir ad\u0131mlara b\u00f6lmesini ve her ad\u0131m\u0131 s\u0131rayla tamamlamas\u0131n\u0131 isteyebiliriz. Bu, modelin karma\u015f\u0131k problemleri sistematik bir \u015fekilde ele almas\u0131na yard\u0131mc\u0131 olur.<\/p>\n
\nPrompt: \"Bir blog yaz\u0131s\u0131 tasla\u011f\u0131 olu\u015ftur: 'Yapay Zekan\u0131n Gelece\u011fi' konusunda.\n1. Ba\u015fl\u0131k \u00f6nerileri sun.\n2. Giri\u015f paragraf\u0131n\u0131 yaz.\n3. Ana ba\u015fl\u0131klar\u0131 ve her ba\u015fl\u0131k alt\u0131nda 2-3 maddeyi listele.\n4. Sonu\u00e7 paragraf\u0131n\u0131 \u00f6zetle.\"\n<\/pre>\n<\/code><\/p>\n
Rol Atama ve Perspektif De\u011fi\u015fimi<\/h4>\n
Modeli belirli bir role b\u00fcr\u00fcnmeye te\u015fvik etmek (\u00f6rne\u011fin, \"bir bilim insan\u0131 gibi d\u00fc\u015f\u00fcn\", \"bir ele\u015ftirmen olarak analiz et\"), farkl\u0131 bak\u0131\u015f a\u00e7\u0131lar\u0131 sunmas\u0131n\u0131 ve daha derinlemesine analizler yapmas\u0131n\u0131 sa\u011flayabilir. Bu, modelin e\u011fitim verilerindeki farkl\u0131 bilgi ve d\u00fc\u015f\u00fcnce tarzlar\u0131n\u0131 aktive etmesine yard\u0131mc\u0131 olur.<\/p>\n
\u00d6rnekleme ve \u0130teratif \u0130yile\u015ftirme<\/h4>\n
Modelden birden fazla \u00e7\u00f6z\u00fcm veya yakla\u015f\u0131m \u00fcretmesini isteyip, ard\u0131ndan bu se\u00e7enekleri de\u011ferlendirmesini veya en iyisini se\u00e7mesini isteyebiliriz. Ayr\u0131ca, modelin \u00fcretti\u011fi bir \u00e7\u0131kt\u0131y\u0131 al\u0131p, geri bildirimle tekrar i\u015fleyerek daha iyi bir sonu\u00e7 elde etmesini sa\u011flayabiliriz. Bu iteratif s\u00fcre\u00e7, derin ara\u015ft\u0131rman\u0131n temel bir par\u00e7as\u0131d\u0131r.<\/p>\n
S\u0131\u011f ve Derin Yakla\u015f\u0131mlar\u0131n Kar\u015f\u0131la\u015ft\u0131rmas\u0131 ve Optimizasyon<\/h3>\n
S\u0131\u011f ve derin ajan yakla\u015f\u0131mlar\u0131n\u0131n her birinin kendine \u00f6zg\u00fc avantajlar\u0131 ve dezavantajlar\u0131 vard\u0131r. Do\u011fru yakla\u015f\u0131m\u0131 se\u00e7mek, g\u00f6revin do\u011fas\u0131na, performans gereksinimlerine ve maliyet k\u0131s\u0131tlamalar\u0131na ba\u011fl\u0131d\u0131r.<\/p>\n
Performans ve Kaynak Maliyeti<\/h4>\n\n- S\u0131\u011f Ajanlar:<\/strong> Daha h\u0131zl\u0131 yan\u0131t s\u00fcreleri sunar ve genellikle daha az hesaplama kayna\u011f\u0131 t\u00fcketir. Basit g\u00f6revler i\u00e7in maliyet-etkindir.<\/li>\n
- Derin Ajanlar:<\/strong> Daha uzun yan\u0131t s\u00fcreleri gerektirir ve daha fazla hesaplama kayna\u011f\u0131 (daha fazla token \u00fcretimi) t\u00fcketir. Karma\u015f\u0131k g\u00f6revler i\u00e7in daha y\u00fcksek maliyetlidir.<\/li>\n<\/ul>\n
Do\u011fruluk ve G\u00fcvenilirlik Dengesi<\/h4>\n
S\u0131\u011f ajanlar, basit bilgi \u00e7ekimi g\u00f6revlerinde y\u00fcksek do\u011fruluk sa\u011flayabilirken, karma\u015f\u0131k \u00e7\u0131kar\u0131mlar gerektiren durumlarda yan\u0131lg\u0131lara d\u00fc\u015fme olas\u0131l\u0131klar\u0131 daha y\u00fcksektir. Derin ajanlar ise, \u00e7ok ad\u0131ml\u0131 ak\u0131l y\u00fcr\u00fctme ve kendi kendini d\u00fczeltme yetenekleri sayesinde daha y\u00fcksek do\u011fruluk ve g\u00fcvenilirlik sunar, ancak bu her zaman garantili de\u011fildir ve iyi prompt m\u00fchendisli\u011fi gerektirir.<\/p>\n
Uygulama Alanlar\u0131na G\u00f6re Se\u00e7im<\/h4>\n
A\u015fa\u011f\u0131daki tablo, iki yakla\u015f\u0131m\u0131n uygun oldu\u011fu senaryolar\u0131 \u00f6zetlemektedir:<\/p>\n
\n\n\n\u00d6zellik<\/th>\n S\u0131\u011f Ajanlar<\/th>\n Derin Ajanlar<\/th>\n<\/tr>\n<\/thead>\n \n\nH\u0131z<\/td>\n Y\u00fcksek<\/td>\n Orta - D\u00fc\u015f\u00fck<\/td>\n<\/tr>\n \nMaliyet<\/td>\n D\u00fc\u015f\u00fck<\/td>\n Y\u00fcksek<\/td>\n<\/tr>\n \nKarma\u015f\u0131kl\u0131k<\/td>\n D\u00fc\u015f\u00fck<\/td>\n Y\u00fcksek<\/td>\n<\/tr>\n \nDo\u011fruluk<\/td>\n Basit g\u00f6revlerde y\u00fcksek, karma\u015f\u0131kta d\u00fc\u015f\u00fck<\/td>\n Karma\u015f\u0131k g\u00f6revlerde potansiyel olarak y\u00fcksek<\/td>\n<\/tr>\n \nUygulama Alanlar\u0131<\/td>\n Soru-Cevap, \u00f6zetleme, basit \u00e7eviri<\/td>\n Problem \u00e7\u00f6zme, kodlama, yarat\u0131c\u0131 yaz\u0131m, ara\u015ft\u0131rma<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nGelecek Perspektifleri ve Zorluklar<\/h3>\n
GPT benzeri sistemlerde derin ara\u015ft\u0131rma yetenekleri hala geli\u015fme a\u015famas\u0131ndad\u0131r ve \u00f6n\u00fcnde hem b\u00fcy\u00fck f\u0131rsatlar hem de \u00f6nemli zorluklar bulunmaktad\u0131r.<\/p>\n
Otonom Ajanlar ve Geli\u015fmi\u015f Mimari<\/h4>\n
Gelecekte, LLM'lerin daha otonom hale gelmesi ve daha karma\u015f\u0131k, uzun soluklu g\u00f6revleri insan m\u00fcdahalesi olmadan y\u00fcr\u00fctebilmesi beklenmektedir. Bu, daha geli\u015fmi\u015f ak\u0131l y\u00fcr\u00fctme mimarileri, bellek y\u00f6netimi ve \u00f6\u011frenme yetenekleri gerektirecektir. Multi-modal ajanlar, farkl\u0131 veri t\u00fcrlerini (metin, g\u00f6r\u00fcnt\u00fc, ses) birle\u015ftirerek daha zengin bir \"ara\u015ft\u0131rma\" deneyimi sunabilir.<\/p>\n
G\u00fcvenilirlik ve \u015eeffafl\u0131k Sorunlar\u0131<\/h4>\n
Derin ajanlar daha do\u011fru sonu\u00e7lar \u00fcretebilse de, \"neden\" sorusunun cevab\u0131n\u0131 vermek, yani ak\u0131l y\u00fcr\u00fctme s\u00fcre\u00e7lerini \u015feffaf bir \u015fekilde a\u00e7\u0131klamak hala bir zorluktur. Ayr\u0131ca, modelin yan\u0131lsama (hallucination) e\u011filimini tamamen ortadan kald\u0131rmak ve her zaman g\u00fcvenilir sonu\u00e7lar \u00fcretmesini sa\u011flamak kritik bir ara\u015ft\u0131rma alan\u0131d\u0131r.<\/p>\n
\u0130nsan-Ajan \u0130\u015fbirli\u011finin Gelece\u011fi<\/h4>\n
En verimli senaryo, s\u0131\u011f ve derin ajan yakla\u015f\u0131mlar\u0131n\u0131n hibrit bir \u015fekilde kullan\u0131lmas\u0131 ve insan uzmanl\u0131\u011f\u0131yla birle\u015ftirilmesidir. \u0130nsanlar, karma\u015f\u0131k g\u00f6revleri tan\u0131mlayabilir, ajanlara y\u00f6n verebilir ve \u00fcretilen sonu\u00e7lar\u0131 denetleyebilirken, ajanlar da h\u0131zl\u0131 bilgi i\u015fleme ve \u00e7ok ad\u0131ml\u0131 ak\u0131l y\u00fcr\u00fctme yetenekleriyle insanlar\u0131 destekleyebilir. Bu i\u015fbirli\u011fi, gelecekteki ara\u015ft\u0131rma ve geli\u015ftirme s\u00fcre\u00e7lerinin temelini olu\u015fturacakt\u0131r.<\/p>\n
Sonu\u00e7<\/h3>\n
GPT benzeri sistemlerde s\u0131\u011f ve derin ajan yakla\u015f\u0131mlar\u0131, yapay zekan\u0131n problem \u00e7\u00f6zme yeteneklerinin iki farkl\u0131 y\u00fcz\u00fcn\u00fc temsil etmektedir. S\u0131\u011f ajanlar h\u0131z ve verimlilik sa\u011flarken, derin ajanlar karma\u015f\u0131kl\u0131k, do\u011fruluk ve g\u00fcvenilirlik sunar. Derin ara\u015ft\u0131rman\u0131n temelinde, modelin i\u00e7selle\u015ftirilmi\u015f bilgisini prompt m\u00fchendisli\u011fi teknikleriyle tetikleyerek \u00e7ok ad\u0131ml\u0131, iteratif ve hatta kendini d\u00fczelten bir ak\u0131l y\u00fcr\u00fctme s\u00fcreci yatar. Gelecekte, bu yeteneklerin daha da geli\u015fmesiyle birlikte, yapay zeka ajanlar\u0131, insanl\u0131\u011f\u0131n kar\u015f\u0131la\u015ft\u0131\u011f\u0131 en zorlu problemlerin \u00e7\u00f6z\u00fcm\u00fcnde vazge\u00e7ilmez birer ortak haline gelecektir. Ancak bu yolculukta \u015feffafl\u0131k, g\u00fcvenilirlik ve etik kullan\u0131m gibi konular\u0131n s\u00fcrekli olarak ele al\u0131nmas\u0131 gerekmektedir.<\/p>\n
SSS (S\u0131k Sorulan Sorular)<\/h3>\n1. Shallow Agent ve Deep Agent aras\u0131ndaki temel fark nedir?<\/h4>\n
Temel fark, ak\u0131l y\u00fcr\u00fctme ad\u0131mlar\u0131n\u0131n say\u0131s\u0131 ve karma\u015f\u0131kl\u0131\u011f\u0131d\u0131r. Shallow Agent'lar genellikle tek ad\u0131ml\u0131, do\u011frudan yan\u0131tlar verirken, Deep Agent'lar birden fazla ad\u0131mdan olu\u015fan, planlama, de\u011ferlendirme ve d\u00fczeltme i\u00e7eren karma\u015f\u0131k ak\u0131l y\u00fcr\u00fctme s\u00fcre\u00e7leri kullan\u0131r.<\/p>\n
2. GPT modelleri do\u011fal olarak hem shallow hem de deep ak\u0131l y\u00fcr\u00fctme yapabilir mi?<\/h4>\n
Evet, GPT modelleri her iki yetene\u011fe de sahiptir. Modelin hangi modu kullanaca\u011f\u0131 b\u00fcy\u00fck \u00f6l\u00e7\u00fcde verilen prompt'un yap\u0131s\u0131na ve karma\u015f\u0131kl\u0131\u011f\u0131na ba\u011fl\u0131d\u0131r. \u0130yi tasarlanm\u0131\u015f prompt'lar, modelin derin ak\u0131l y\u00fcr\u00fctme yeteneklerini tetikleyebilir.<\/p>\n
3. Chain-of-Thought (CoT) prompt'lar\u0131 neden Deep Agent davran\u0131\u015f\u0131n\u0131 tetikler?<\/h4>\n
CoT prompt'lar\u0131, modelden sadece cevab\u0131 de\u011fil, ayn\u0131 zamanda cevaba nas\u0131l ula\u015ft\u0131\u011f\u0131n\u0131 g\u00f6steren ara ad\u0131mlar\u0131 da a\u00e7\u0131klamas\u0131n\u0131 isteyerek, modelin i\u00e7sel olarak daha fazla \"d\u00fc\u015f\u00fcnce\" token'\u0131 \u00fcretmesini te\u015fvik eder. Bu ara ad\u0131mlar, bir sonraki ad\u0131m\u0131 tahmin etmek i\u00e7in girdi g\u00f6revi g\u00f6rerek derinlemesine bir ak\u0131l y\u00fcr\u00fctme zinciri olu\u015fturur.<\/p>\n
4. Deep Agent'lar\u0131n kullan\u0131m\u0131 Shallow Agent'lara g\u00f6re daha m\u0131 maliyetlidir?<\/h4>\n
Genellikle evet. Deep Agent'lar, \u00e7ok ad\u0131ml\u0131 ak\u0131l y\u00fcr\u00fctme i\u00e7in daha fazla token \u00fcretme e\u011filimindedir. Her token \u00fcretimi bir maliyetle geldi\u011fi i\u00e7in, Deep Agent yakla\u015f\u0131mlar\u0131 genellikle daha y\u00fcksek API kullan\u0131m maliyetlerine yol a\u00e7ar.<\/p>\n
5. Bir g\u00f6revin Shallow m\u0131 yoksa Deep Agent yakla\u015f\u0131m\u0131yla m\u0131 \u00e7\u00f6z\u00fclmesi gerekti\u011fine nas\u0131l karar verilir?<\/h4>\n
G\u00f6revin karma\u015f\u0131kl\u0131\u011f\u0131, do\u011fruluk gereksinimi, h\u0131z ihtiyac\u0131 ve b\u00fct\u00e7e k\u0131s\u0131tlamalar\u0131 bu karar\u0131 etkiler. Basit bilgi \u00e7ekimi veya h\u0131zl\u0131 \u00f6zetleme i\u00e7in Shallow Agent yeterliyken, karma\u015f\u0131k problem \u00e7\u00f6zme, kodlama veya yarat\u0131c\u0131 yaz\u0131m gibi g\u00f6revler i\u00e7in Deep Agent yakla\u015f\u0131m\u0131 daha uygun ve etkilidir.<\/p>\n","protected":false},"excerpt":{"rendered":"Yapay zeka ba\u011flam\u0131nda bir “ajan”, \u00e7evresini alg\u0131layan ve hedeflerine ula\u015fmak i\u00e7in eylemlerde bulunan otonom bir varl\u0131kt\u0131r. GPT benzeri sistemler de m…","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-36632","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\n
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