{"id":12672,"date":"2026-08-06T00:55:55","date_gmt":"2026-08-05T21:55:55","guid":{"rendered":"https:\/\/gtmteknoloji.com\/b2b\/rag-nedir\/"},"modified":"2026-08-06T01:05:38","modified_gmt":"2026-08-05T22:05:38","slug":"rag-nedir","status":"publish","type":"post","link":"https:\/\/gtmteknoloji.com\/b2b\/rag-nedir\/","title":{"rendered":"RAG Nedir? Kurumsal Yapay Zekada Eri\u015fim Destekli \u00dcretim Rehberi"},"content":{"rendered":"<div class=\"gtm-rag\">\n<h1 class=\"r-title\">RAG Nedir? Kurumsal Yapay Zekada Eri\u015fim Destekli \u00dcretim<\/h1>\n<div class=\"r-meta\">\n  <span>Yapay Zeka Altyap\u0131s\u0131<\/span><br \/>\n  <span>Okuma s\u00fcresi ~9 dakika<\/span><br \/>\n  <span>GTM Teknoloji<\/span>\n<\/div>\n<p class=\"r-intro\">Kurumsal yapay zeka projelerinde en s\u0131k duyulan c\u00fcmle \u015fudur: \u201cModel g\u00fczel konu\u015fuyor ama bizim verimizi bilmiyor.\u201d RAG, tam olarak bu bo\u015flu\u011fu kapatan y\u00f6ntemin ad\u0131. Bu yaz\u0131da RAG\u2019in ne oldu\u011funu, nas\u0131l \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131, hangi donan\u0131m\u0131 gerektirdi\u011fini ve kurumunuzda nereden ba\u015flaman\u0131z gerekti\u011fini u\u00e7tan uca anlat\u0131yoruz.<\/p>\n<div class=\"r-toc\">\n  <b>\u0130\u00e7indekiler<\/b><\/p>\n<ol>\n<li><a href=\"#rag-nedir\">RAG nedir?<\/a><\/li>\n<li><a href=\"#neden\">Neden ihtiya\u00e7 duyuluyor?<\/a><\/li>\n<li><a href=\"#nasil\">RAG nas\u0131l \u00e7al\u0131\u015f\u0131r: d\u00f6rt ad\u0131m<\/a><\/li>\n<li><a href=\"#mimari\">Mimarinin \u00fc\u00e7 a\u015famas\u0131<\/a><\/li>\n<li><a href=\"#anlamsal\">Anlamsal arama ve vekt\u00f6r veritaban\u0131<\/a><\/li>\n<li><a href=\"#ileri\">\u0130leri d\u00fczey teknikler ve ajan tabanl\u0131 RAG<\/a><\/li>\n<li><a href=\"#fine-tuning\">RAG m\u0131, fine-tuning mi?<\/a><\/li>\n<li><a href=\"#kullanim\">Kurumsal kullan\u0131m alanlar\u0131<\/a><\/li>\n<li><a href=\"#donanim\">Hangi donan\u0131m gerekir?<\/a><\/li>\n<li><a href=\"#zorluk\">Zorluklar ve \u00e7\u00f6z\u00fcmleri<\/a><\/li>\n<li><a href=\"#baslangic\">Nereden ba\u015flamal\u0131?<\/a><\/li>\n<li><a href=\"#sss\">S\u0131k sorulan sorular<\/a><\/li>\n<\/ol>\n<\/div>\n<h2 id=\"rag-nedir\">RAG nedir?<\/h2>\n<p><strong>RAG (Retrieval-Augmented Generation \u2014 eri\u015fim destekli \u00fcretim)<\/strong>, bir b\u00fcy\u00fck dil modelinin (LLM) d\u0131\u015f bir veri kayna\u011f\u0131na ba\u011flanarak alana \u00f6zg\u00fc ve g\u00fcncel yan\u0131tlar \u00fcretmesini sa\u011flayan yapay zeka tekni\u011fidir. Model, yaln\u0131zca e\u011fitim verisinden hat\u0131rlad\u0131klar\u0131yla konu\u015fmak yerine; soruyla ilgili bilgiyi kurumun kendi belgelerinden bulur, bu bilgiyi ba\u011flam\u0131na ekler ve yan\u0131t\u0131n\u0131 buna dayand\u0131r\u0131r.<\/p>\n<p>K\u0131sacas\u0131 RAG, dil modeline \u201c\u00f6nce oku, sonra cevapla\u201d disiplinini kazand\u0131r\u0131r. Modelin yeniden e\u011fitilmesine gerek kalmadan uzmanl\u0131k bilgisi sisteme dahil edilir \u2014 bu da hesaplama kayna\u011f\u0131ndan ciddi tasarruf demektir.<\/p>\n<h2 id=\"neden\">Neden ihtiya\u00e7 duyuluyor?<\/h2>\n<p>B\u00fcy\u00fck dil modelleri g\u00fc\u00e7l\u00fcd\u00fcr, ancak bilgileri \u00f6n e\u011fitim verileriyle s\u0131n\u0131rl\u0131d\u0131r. Kendi belgelerine ve verisine dayanan yapay zeka uygulamalar\u0131na ihtiya\u00e7 duyan kurumlar i\u00e7in bu ciddi bir k\u0131s\u0131tt\u0131r. \u00dc\u00e7 temel sorun ortaya \u00e7\u0131kar:<\/p>\n<ul class=\"r-clean\">\n<li><strong>Kurum verisini bilmez.<\/strong> Mevzuat\u0131n\u0131z, \u00fcr\u00fcn katalo\u011funuz, ge\u00e7mi\u015f yaz\u0131\u015fmalar\u0131n\u0131z veya teknik dok\u00fcmantasyonunuz modelin e\u011fitim setinde yoktur.<\/li>\n<li><strong>G\u00fcncel de\u011fildir.<\/strong> Model belirli bir tarihte e\u011fitilmi\u015ftir; sonras\u0131nda de\u011fi\u015fen fiyat, prosed\u00fcr veya mevzuattan haberi yoktur.<\/li>\n<li><strong>Hal\u00fcsinasyon \u00fcretir.<\/strong> Bilmedi\u011fi bir konuda emin bir tonla yanl\u0131\u015f bilgi uydurabilir \u2014 kurumsal kullan\u0131mda kabul edilemez bir risktir.<\/li>\n<\/ul>\n<p>RAG bu \u00fc\u00e7 sorunu tek hamlede ele al\u0131r: yan\u0131t\u0131 kurumun ger\u00e7ek verisine dayand\u0131rarak do\u011frulu\u011fu art\u0131r\u0131r, hal\u00fcsinasyonu azalt\u0131r ve veri g\u00fcncellendi\u011fi anda sistemin yeni bilgiyi kullanmas\u0131n\u0131 sa\u011flar.<\/p>\n<h2 id=\"nasil\">RAG nas\u0131l \u00e7al\u0131\u015f\u0131r: d\u00f6rt ad\u0131m<\/h2>\n<p>Perde arkas\u0131nda ak\u0131\u015f olduk\u00e7a nettir:<\/p>\n<div class=\"r-steps\">\n<div class=\"r-step\"><span class=\"no\">1<\/span><\/p>\n<div><b>Sorgu vekt\u00f6re d\u00f6n\u00fc\u015ft\u00fcr\u00fcl\u00fcr<\/b><\/p>\n<p>Kullan\u0131c\u0131n\u0131n sorusu, bir g\u00f6mme (embedding) modeliyle say\u0131sal bir vekt\u00f6r temsiline \u00e7evrilir. Bu, sorunun \u201canlam\u0131n\u0131n\u201d matematiksel kar\u015f\u0131l\u0131\u011f\u0131d\u0131r.<\/p>\n<\/div>\n<\/div>\n<div class=\"r-step\"><span class=\"no\">2<\/span><\/p>\n<div><b>Anlamca benzer veri aran\u0131r<\/b><\/p>\n<p>Sistem, vekt\u00f6r veritaban\u0131nda bu vekt\u00f6re en yak\u0131n i\u00e7erik par\u00e7alar\u0131n\u0131 arar. Kelime e\u015fle\u015fmesi de\u011fil, anlam yak\u0131nl\u0131\u011f\u0131 aran\u0131r.<\/p>\n<\/div>\n<\/div>\n<div class=\"r-step\"><span class=\"no\">3<\/span><\/p>\n<div><b>En ilgili bilgi ba\u011flama eklenir<\/b><\/p>\n<p>Bulunan en alakal\u0131 par\u00e7alar, modele g\u00f6nderilecek istemin (prompt) i\u00e7ine kullan\u0131c\u0131n\u0131n sorusuyla birlikte yerle\u015ftirilir.<\/p>\n<\/div>\n<\/div>\n<div class=\"r-step\"><span class=\"no\">4<\/span><\/p>\n<div><b>Yan\u0131t \u00fcretilir ve dayand\u0131r\u0131l\u0131r<\/b><\/p>\n<p>Model, yan\u0131t\u0131n\u0131 bu getirilen veriye dayand\u0131rarak \u00fcretir. Kurumsal sistemlerde \u00e7o\u011fu zaman kaynak belgeye ba\u011flant\u0131 da verilir.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<h2 id=\"mimari\">Mimarinin \u00fc\u00e7 a\u015famas\u0131<\/h2>\n<p>Tipik bir RAG hatt\u0131 \u00fc\u00e7 a\u015famada \u00e7al\u0131\u015f\u0131r. Her a\u015fama, sistemin isabetli ve g\u00fcvenilir veri getirmesi a\u00e7\u0131s\u0131ndan kritiktir.<\/p>\n<div class=\"r-phases\">\n<div class=\"r-phase\"><b>1. \u00c7\u0131kar\u0131m (Extraction)<\/b><\/p>\n<p>Kurumsal veri toplan\u0131r, temizlenir, par\u00e7alara ayr\u0131l\u0131r, g\u00f6mme modeliyle vekt\u00f6re d\u00f6n\u00fc\u015ft\u00fcr\u00fcl\u00fcr ve vekt\u00f6r veritaban\u0131nda indekslenir.<\/p>\n<\/div>\n<div class=\"r-phase\"><b>2. Getirme (Retrieval)<\/b><\/p>\n<p>Vekt\u00f6r ve anahtar kelime arama teknikleriyle ilgili veri bulunur. Yeniden s\u0131ralama (reranking) modeli en alakal\u0131 olan\u0131 \u00f6ne \u00e7\u0131kar\u0131r.<\/p>\n<\/div>\n<div class=\"r-phase\"><b>3. \u00dcretim (Generation)<\/b><\/p>\n<p>LLM, kullan\u0131c\u0131n\u0131n istemiyle getirilen veriyi birle\u015ftirerek hem anlamsal hem ba\u011flamsal olarak isabetli yan\u0131t \u00fcretir.<\/p>\n<\/div>\n<\/div>\n<h3>\u00c7\u0131kar\u0131m a\u015famas\u0131nda neler olur?<\/h3>\n<p>\u00d6nce veri toplan\u0131r ve ayr\u0131\u015ft\u0131r\u0131l\u0131r: belgeler, PDF\u2019ler, \u00fcr\u00fcn kataloglar\u0131, g\u00f6rseller, hatta ses kay\u0131tlar\u0131n\u0131n metin d\u00f6k\u00fcmleri. Metin genellikle paragraf veya b\u00f6l\u00fcmlere <strong>par\u00e7alara ayr\u0131l\u0131r (chunking)<\/strong> \u2014 b\u00f6ylece ba\u011flam penceresi verimli kullan\u0131l\u0131r ve getirme isabeti artar. Bu a\u015famada veri kalitesi belirleyicidir: do\u011fru meta veri ve tekrarlar\u0131n temizlenmesi olmadan, en geli\u015fmi\u015f model bile zorlan\u0131r.<\/p>\n<p>Veri toplama y\u00f6ntemi kayna\u011fa g\u00f6re se\u00e7ilir:<\/p>\n<ul class=\"r-clean\">\n<li><strong>Toplu (batch):<\/strong> Dura\u011fan veya nadiren de\u011fi\u015fen veri k\u00fcmeleri i\u00e7in. G\u00f6mmeler toplu halde \u00fcretilir.<\/li>\n<li><strong>Ak\u0131\u015f (streaming):<\/strong> S\u00fcrekli g\u00fcncellenen veri i\u00e7in (haber ak\u0131\u015f\u0131, i\u015flem kay\u0131tlar\u0131, sosyal medya).<\/li>\n<\/ul>\n<p>Ard\u0131ndan g\u00f6mme modeli, i\u00e7eri\u011fi <strong>vekt\u00f6r g\u00f6mmelerine<\/strong> d\u00f6n\u00fc\u015ft\u00fcr\u00fcr. Bunlar; metin, g\u00f6rsel veya sesin \u00e7ok boyutlu bir uzaya yerle\u015ftirilmi\u015f say\u0131sal temsilleridir. Anlamca yak\u0131n i\u00e7erikler bu uzayda birbirine yak\u0131n konumlan\u0131r \u2014 h\u0131zl\u0131 ve isabetli aramay\u0131 m\u00fcmk\u00fcn k\u0131lan \u015fey budur.<\/p>\n<h3>Getirme a\u015famas\u0131nda neler olur?<\/h3>\n<p>S\u00fcre\u00e7 \u00e7o\u011fu zaman <strong>sorgu yeniden yaz\u0131m\u0131<\/strong> ile ba\u015flar: sorgu e\u015f anlaml\u0131larla geni\u015fletilir, belirsizlikler giderilir veya \u00f6nceki konu\u015fmadan ba\u011flam eklenir. Sonra sorgu, ayn\u0131 g\u00f6mme modeliyle vekt\u00f6re \u00e7evrilir \u2014 indeksleme an\u0131 ile sorgu an\u0131 aras\u0131nda model tutarl\u0131l\u0131\u011f\u0131 \u015fartt\u0131r.<\/p>\n<p>Son olarak <strong>benzerlik aramas\u0131<\/strong> yap\u0131l\u0131r: kosin\u00fcs benzerli\u011fi, \u00d6klid mesafesi veya nokta \u00e7arp\u0131m\u0131 gibi \u00f6l\u00e7\u00fctlerle en alakal\u0131 ilk <em>k<\/em> par\u00e7a getirilir. Yakla\u015f\u0131k en yak\u0131n kom\u015fu (ANN) algoritmalar\u0131 bu ad\u0131m\u0131 h\u0131zland\u0131r\u0131r.<\/p>\n<div class=\"r-note\">\n  <b>Yeniden s\u0131ralama (reranking) neden \u00f6nemli?<\/b><br \/>\n  \u0130lk getirme ad\u0131m\u0131 geni\u015f bir aday k\u00fcmesi d\u00f6nd\u00fcr\u00fcr. Yeniden s\u0131ralama modeli bu adaylar\u0131 g\u00fcncellik, alan uygunlu\u011fu, meta veri \u00f6rt\u00fc\u015fmesi veya anlamsal benzerlik gibi sinyallere g\u00f6re yeniden s\u0131ralar. B\u00f6ylece model en kaliteli bilgiyi en \u00f6nde i\u015fler \u2014 bu tek ad\u0131m, nihai yan\u0131t do\u011frulu\u011funu belirgin bi\u00e7imde y\u00fckseltir.\n<\/div>\n<h3>\u00dcretim a\u015famas\u0131nda neler olur?<\/h3>\n<p>Getirilen par\u00e7alar kullan\u0131c\u0131n\u0131n sorusuyla birlikte modelin istemine eklenir ve yan\u0131t \u00fcretilir. Bilgi yo\u011fun alanlarda (t\u0131p, hukuk, ara\u015ft\u0131rma) RAG birden \u00e7ok kaynaktan gelen bilgiyi \u00f6zetleyip maddeler halinde sunabilir. Y\u00fcksek riskli uygulamalarda ise bir <strong>\u00e7\u0131kt\u0131 do\u011frulama<\/strong> katman\u0131 eklenir:<\/p>\n<ul class=\"r-clean\">\n<li>\u00dcretilen yan\u0131t\u0131n getirilen metinle tutarl\u0131l\u0131\u011f\u0131n\u0131n kar\u015f\u0131la\u015ft\u0131r\u0131lmas\u0131<\/li>\n<li>\u0130kinci bir modelin ilk modelin iddialar\u0131n\u0131 do\u011frulamas\u0131<\/li>\n<li>Yan\u0131t\u0131n kaynaklar\u0131yla birlikte kayda al\u0131nmas\u0131 (denetlenebilirlik ve uyum i\u00e7in)<\/li>\n<\/ul>\n<h2 id=\"anlamsal\">Anlamsal arama ve vekt\u00f6r veritaban\u0131<\/h2>\n<p>RAG\u2019i anlamak i\u00e7in iki kavram\u0131 ay\u0131rmak gerekir.<\/p>\n<div class=\"r-tblwrap\">\n<table>\n<thead>\n<tr>\n<th>\u00d6l\u00e7\u00fct<\/th>\n<th>Anahtar kelime aramas\u0131<\/th>\n<th>Anlamsal arama<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Ne arar?<\/td>\n<td>Girilen kelimelerin birebir e\u015fle\u015fmesi<\/td>\n<td>Sorgunun anlam\u0131 ve niyeti<\/td>\n<\/tr>\n<tr>\n<td>E\u015f anlaml\u0131<\/td>\n<td>Anlamaz<\/td>\n<td>Anlar ve geni\u015fletir<\/td>\n<\/tr>\n<tr>\n<td>\u00d6rnek sonu\u00e7<\/td>\n<td>Yaln\u0131zca ayn\u0131 ifadeyi i\u00e7eren belgeler<\/td>\n<td>\u0130lgili ama farkl\u0131 kelimelerle yaz\u0131lm\u0131\u015f belgeler<\/td>\n<\/tr>\n<tr>\n<td>Y\u00f6ntem<\/td>\n<td>BM25, TF-IDF<\/td>\n<td>Vekt\u00f6r benzerli\u011fi (kosin\u00fcs, ANN)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>K\u0131saca: <strong>anahtar kelime aramas\u0131 kelimeyi, anlamsal arama anlam\u0131 arar.<\/strong> Pratikte en iyi sonu\u00e7 ikisinin birle\u015fiminden (<em>hibrit arama<\/em>) \u00e7\u0131kar.<\/p>\n<p>Bir di\u011fer ayr\u0131m da bilgi getirme ile RAG aras\u0131ndad\u0131r. Klasik bilgi getirme size bir belge listesi d\u00f6nd\u00fcr\u00fcr ve yorumlamay\u0131 size b\u0131rak\u0131r. RAG ise getirdi\u011fi i\u00e7eri\u011fi kullanarak <strong>do\u011frudan, ba\u011flama duyarl\u0131 bir yan\u0131t \u00fcretir<\/strong> \u2014 manuel okuma y\u00fck\u00fcn\u00fc ortadan kald\u0131r\u0131r.<\/p>\n<h2 id=\"ileri\">\u0130leri d\u00fczey teknikler ve ajan tabanl\u0131 RAG<\/h2>\n<ul class=\"r-clean\">\n<li><strong>Hibrit getirme:<\/strong> Vekt\u00f6r aramas\u0131n\u0131 BM25 gibi klasik tekniklerle birle\u015ftirir; hem anlam hem tam e\u015fle\u015fme yakalan\u0131r.<\/li>\n<li><strong>Uzun ba\u011flam getirme:<\/strong> Binlerce jetonu tek istemde i\u015fleyebilen modellerle \u00e7ok say\u0131da kaynak birlikte de\u011ferlendirilir. Ara\u015ft\u0131rma, hukuk ve teknik alanlarda de\u011ferlidir; ancak hesaplama maliyeti ve bellek kullan\u0131m\u0131 artar.<\/li>\n<li><strong>Ba\u011flamsal getirme:<\/strong> Her par\u00e7aya ait oldu\u011fu belge, \u00e7evresinin \u00f6zeti veya indeks tarihi gibi meta veriler eklenir. Kod depolar\u0131, hukuki metinler ve bilimsel makaleler gibi \u00e7ok kaynakl\u0131 karma\u015f\u0131k belgelerde isabeti belirgin art\u0131r\u0131r.<\/li>\n<\/ul>\n<p><strong>Ajan tabanl\u0131 (agentic) RAG<\/strong> ise i\u015fi bir ad\u0131m \u00f6teye ta\u015f\u0131r: ortak bir hedef i\u00e7in birlikte \u00e7al\u0131\u015fan birden \u00e7ok yapay zeka ajan\u0131, \u00e7oklu getirme\u2013ak\u0131l y\u00fcr\u00fctme\u2013iyile\u015ftirme ge\u00e7i\u015fleri yapar. Yaz\u0131l\u0131m tasar\u0131m\u0131, BT otomasyonu, kod \u00fcretimi, m\u00fc\u015fteri deste\u011fi ve kurumsal bilgi y\u00f6netimi gibi kapsaml\u0131 veri i\u015fleme gerektiren senaryolarda tercih edilir.<\/p>\n<h2 id=\"fine-tuning\">RAG m\u0131, fine-tuning mi?<\/h2>\n<p>Kurumlar\u0131n en s\u0131k sordu\u011fu sorulardan biri budur. K\u0131sa cevap: \u00e7o\u011fu senaryoda \u00f6nce RAG.<\/p>\n<div class=\"r-tblwrap\">\n<table>\n<thead>\n<tr>\n<th>\u00d6l\u00e7\u00fct<\/th>\n<th>RAG<\/th>\n<th>Fine-tuning<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Ne de\u011fi\u015fir?<\/td>\n<td>D\u0131\u015f veri kayna\u011f\u0131<\/td>\n<td>Modelin a\u011f\u0131rl\u0131klar\u0131<\/td>\n<\/tr>\n<tr>\n<td>Veri g\u00fcncellendi\u011finde<\/td>\n<td>An\u0131nda yans\u0131r<\/td>\n<td>Yeniden e\u011fitim gerekir<\/td>\n<\/tr>\n<tr>\n<td>Maliyet<\/td>\n<td>D\u00fc\u015f\u00fck \u2014 model korunur<\/td>\n<td>Y\u00fcksek \u2014 GPU saati ve uzmanl\u0131k<\/td>\n<\/tr>\n<tr>\n<td>Kaynak g\u00f6sterme<\/td>\n<td>M\u00fcmk\u00fcn ve do\u011fal<\/td>\n<td>Zor<\/td>\n<\/tr>\n<tr>\n<td>G\u00fc\u00e7l\u00fc oldu\u011fu yer<\/td>\n<td>G\u00fcncel ve kuruma \u00f6zel bilgi<\/td>\n<td>\u00dcslup, format, alan jargonu<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>\u0130kisi rakip de\u011fildir: pratikte RAG ile ba\u015flan\u0131r, model kurumun diline ve \u00e7\u0131kt\u0131 format\u0131na tam uymuyorsa \u00fczerine hafif bir fine-tuning eklenir.<\/p>\n<h2 id=\"kullanim\">Kurumsal kullan\u0131m alanlar\u0131<\/h2>\n<div class=\"r-uses\">\n<div class=\"r-use\"><b>Kurumsal arama ve bilgi y\u00f6netimi<\/b><span>\u015eirket belgeleri, wiki\u2019ler ve bilgi tabanlar\u0131ndan bilgi getirip sentezleyerek cevap arama s\u00fcresini k\u0131salt\u0131r.<\/span><\/div>\n<div class=\"r-use\"><b>M\u00fc\u015fteri deste\u011fi ve sohbet botlar\u0131<\/b><span>\u015eirket politikalar\u0131, SSS ve sorun giderme k\u0131lavuzlar\u0131n\u0131 getirerek g\u00fcncel ve do\u011fru yan\u0131t verir.<\/span><\/div>\n<div class=\"r-use\"><b>Finans ve piyasa analizi<\/b><span>Piyasa e\u011filimleri, \u015firket raporlar\u0131 ve d\u00fczenleyici bildirimleri \u00f6zetleyerek karar deste\u011fi sa\u011flar.<\/span><\/div>\n<div class=\"r-use\"><b>Sa\u011fl\u0131k ve t\u0131bbi ara\u015ft\u0131rma<\/b><span>G\u00fcncel \u00e7al\u0131\u015fmalar, klinik k\u0131lavuzlar ve hasta kay\u0131tlar\u0131n\u0131 getirip \u00f6zetleyerek klinisyene destek olur.<\/span><\/div>\n<div class=\"r-use\"><b>Hukuk ve uyum<\/b><span>Mevzuat, i\u00e7tihat ve s\u00f6zle\u015fme metinlerini sentezleyerek ara\u015ft\u0131rma ve s\u00f6zle\u015fme analizini h\u0131zland\u0131r\u0131r.<\/span><\/div>\n<div class=\"r-use\"><b>Kod ve teknik dok\u00fcmantasyon<\/b><span>Depolardan kod par\u00e7ac\u0131klar\u0131, API dok\u00fcmanlar\u0131 ve \u00e7\u00f6z\u00fcm \u00f6rnekleri getirerek geli\u015ftiriciye yard\u0131m eder.<\/span><\/div>\n<\/div>\n<p>RAG yaln\u0131zca metinle s\u0131n\u0131rl\u0131 de\u011fildir. Bilgisayarl\u0131 g\u00f6r\u00fc ve konu\u015fma i\u015fleme modelleriyle g\u00f6rsel, ses ve video da g\u00f6mmelere d\u00f6n\u00fc\u015ft\u00fcr\u00fclebilir; b\u00f6ylece <strong>\u00e7apraz modal arama<\/strong> m\u00fcmk\u00fcn olur. \u00d6rne\u011fin bir e-ticaret platformu hem \u00fcr\u00fcn a\u00e7\u0131klamalar\u0131n\u0131 hem g\u00f6rsellerini g\u00f6merek \u201cbu foto\u011frafa benzer \u00fcr\u00fcnleri bul\u201d sorgusunu yan\u0131tlayabilir. \u00c7ok dilli modeller sayesinde T\u00fcrk\u00e7e ve yabanc\u0131 dildeki belgeler tek sistemde birlikte sorgulanabilir.<\/p>\n<h2 id=\"donanim\">Hangi donan\u0131m gerekir?<\/h2>\n<p>RAG bir yaz\u0131l\u0131m mimarisidir, ama \u00fc\u00e7 noktada donan\u0131ma yaslan\u0131r: <strong>g\u00f6mme \u00fcretimi<\/strong>, <strong>vekt\u00f6r arama<\/strong> ve <strong>model \u00e7\u0131kar\u0131m\u0131<\/strong>. \u00dc\u00e7\u00fc de GPU h\u0131zland\u0131rmas\u0131ndan belirgin fayda g\u00f6r\u00fcr. \u00d6l\u00e7e\u011fe g\u00f6re kabaca \u015fu yol izlenir:<\/p>\n<div class=\"r-tblwrap\">\n<table>\n<thead>\n<tr>\n<th>A\u015fama<\/th>\n<th>Tipik kurulum<\/th>\n<th>Ne sa\u011flar?<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Kavram kan\u0131t\u0131 (PoC)<\/td>\n<td>Tek AI i\u015f istasyonu \u2014 128GB birle\u015fik bellek s\u0131n\u0131f\u0131<\/td>\n<td>S\u0131n\u0131rl\u0131 belge k\u00fcmesiyle ger\u00e7ek veri \u00fczerinde \u00f6l\u00e7\u00fclebilir ilk sonu\u00e7<\/td>\n<\/tr>\n<tr>\n<td>Birim \/ pilot \u00fcretim<\/td>\n<td>1\u20132 GPU\u2019lu sunucu veya masa\u00fcst\u00fc AI sistemi<\/td>\n<td>Tek departman\u0131n g\u00fcnl\u00fck kullan\u0131m\u0131, 10\u201350 e\u015fzamanl\u0131 kullan\u0131c\u0131<\/td>\n<\/tr>\n<tr>\n<td>Kurum \u00e7ap\u0131<\/td>\n<td>\u00c7oklu GPU sunucular, ayr\u0131 vekt\u00f6r veritaban\u0131 d\u00fc\u011f\u00fcm\u00fc<\/td>\n<td>Y\u00fczlerce kullan\u0131c\u0131, milyonlarca belge par\u00e7as\u0131, y\u00fcksek eri\u015filebilirlik<\/td>\n<\/tr>\n<tr>\n<td>U\u00e7 nokta (edge)<\/td>\n<td>Kompakt\/fans\u0131z u\u00e7 sunucular<\/td>\n<td>Ma\u011faza, saha veya \u015fubede yerinde \u00e7al\u0131\u015fan RAG asistan\u0131<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>Pratik bir ba\u015flang\u0131\u00e7 noktas\u0131 olarak <a href=\"\/b2b\/magaza\/edge-ai\/nvidia-dgx-spark\/nvidia-dgx-spark-ai-desktop-masaustu-yapay-zeka-llm-sistemi\/\">NVIDIA DGX Spark<\/a> gibi masa\u00fcst\u00fc AI sistemleri veya <a href=\"\/b2b\/super-ai-station\/\">Supermicro Super AI Station<\/a> tercih edilir; kurum \u00e7ap\u0131na \u00e7\u0131karken <a href=\"\/b2b\/urun-kategori\/gpu-server\/\">GPU sunucu platformlar\u0131na<\/a> ge\u00e7ilir. U\u00e7ta \u00e7al\u0131\u015facak senaryolar i\u00e7in <a href=\"\/b2b\/edge-ai-uc-bilisim\/\">Edge AI sistemleri<\/a> devreye girer.<\/p>\n<h2 id=\"zorluk\">Zorluklar ve \u00e7\u00f6z\u00fcmleri<\/h2>\n<ul class=\"r-clean\">\n<li><strong>Veri kalitesi:<\/strong> Da\u011f\u0131n\u0131k, tekrarl\u0131 veya meta verisi eksik belgeler getirme isabetini d\u00fc\u015f\u00fcr\u00fcr. \u00c7\u00f6z\u00fcm: kurulum \u00f6ncesi veri temizli\u011fi, tutarl\u0131 par\u00e7alama stratejisi ve meta veri standard\u0131.<\/li>\n<li><strong>Par\u00e7alama (chunking) stratejisi:<\/strong> \u00c7ok k\u00fc\u00e7\u00fck par\u00e7alar ba\u011flam\u0131 kaybettirir, \u00e7ok b\u00fcy\u00fck par\u00e7alar g\u00fcr\u00fclt\u00fc ekler. \u00c7\u00f6z\u00fcm: i\u00e7erik t\u00fcr\u00fcne g\u00f6re par\u00e7a boyutu ve \u00f6rt\u00fc\u015fme ayar\u0131, gerekirse ba\u011flamsal getirme.<\/li>\n<li><strong>Gecikme:<\/strong> Getirme + \u00fcretim zinciri yan\u0131t s\u00fcresini uzatabilir. \u00c7\u00f6z\u00fcm: GPU h\u0131zland\u0131rmal\u0131 vekt\u00f6r arama, ANN indeksleri, \u00f6nbellekleme ve model boyutunun senaryoya g\u00f6re se\u00e7ilmesi.<\/li>\n<li><strong>Yetkilendirme:<\/strong> Herkesin her belgeye eri\u015fmemesi gerekir. \u00c7\u00f6z\u00fcm: getirme katman\u0131nda kullan\u0131c\u0131 bazl\u0131 eri\u015fim filtresi \u2014 ki\u015fi yaln\u0131zca yetkili oldu\u011fu i\u00e7erikten yan\u0131t al\u0131r.<\/li>\n<li><strong>\u00d6l\u00e7\u00fclebilirlik:<\/strong> \u201c\u0130yi \u00e7al\u0131\u015f\u0131yor mu?\u201d sorusunun \u00f6l\u00e7\u00fcs\u00fc olmal\u0131. \u00c7\u00f6z\u00fcm: getirme isabeti (recall), yan\u0131t do\u011frulu\u011fu ve kaynak tutarl\u0131l\u0131\u011f\u0131 i\u00e7in de\u011ferlendirme k\u00fcmesi olu\u015fturulmas\u0131.<\/li>\n<\/ul>\n<h2 id=\"baslangic\">Nereden ba\u015flamal\u0131?<\/h2>\n<p>Deneyimimiz \u015funu g\u00f6steriyor: RAG projelerinde ba\u015far\u0131s\u0131zl\u0131\u011f\u0131n en yayg\u0131n sebebi teknoloji de\u011fil, \u00e7ok geni\u015f bir kapsamla ba\u015flamakt\u0131r. \u00d6nerdi\u011fimiz s\u0131ra:<\/p>\n<div class=\"r-steps\">\n<div class=\"r-step\"><span class=\"no\">1<\/span><\/p>\n<div><b>Tek ve net bir soru se\u00e7in<\/b><\/p>\n<p>\u201cT\u00fcm kurum bilgisini yapay zekaya verelim\u201d yerine, \u201cdestek ekibinin en s\u0131k sordu\u011fu 50 soruyu belgelerimizden yan\u0131tlayal\u0131m\u201d gibi \u00f6l\u00e7\u00fclebilir bir hedef belirleyin.<\/p>\n<\/div>\n<\/div>\n<div class=\"r-step\"><span class=\"no\">2<\/span><\/p>\n<div><b>S\u0131n\u0131rl\u0131 veriyle kavram kan\u0131t\u0131 yap\u0131n<\/b><\/p>\n<p>Tek bir sistem ve se\u00e7ili belge k\u00fcmesiyle \u00e7al\u0131\u015f\u0131n. Ger\u00e7ek verinizi kullan\u0131n \u2014 sentetik veriyle al\u0131nan sonu\u00e7 yan\u0131lt\u0131c\u0131d\u0131r.<\/p>\n<\/div>\n<\/div>\n<div class=\"r-step\"><span class=\"no\">3<\/span><\/p>\n<div><b>\u00d6l\u00e7\u00fcn ve iyile\u015ftirin<\/b><\/p>\n<p>Getirme isabetini ve yan\u0131t do\u011frulu\u011funu \u00f6l\u00e7\u00fcn. \u0130yile\u015ftirme \u00e7o\u011fu zaman modelde de\u011fil, veri haz\u0131rl\u0131\u011f\u0131 ve par\u00e7alama stratejisindedir.<\/p>\n<\/div>\n<\/div>\n<div class=\"r-step\"><span class=\"no\">4<\/span><\/p>\n<div><b>Ayn\u0131 y\u0131\u011f\u0131nla \u00f6l\u00e7ekleyin<\/b><\/p>\n<p>Pilotu ayakta tutan yaz\u0131l\u0131m y\u0131\u011f\u0131n\u0131n\u0131 de\u011fi\u015ftirmeden donan\u0131m\u0131 b\u00fcy\u00fct\u00fcn. B\u00f6ylece geli\u015ftirdi\u011finiz uygulama yeniden yaz\u0131lmaz.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"r-note\">\n  <b>Veri egemenli\u011fi notu<\/b><br \/>\n  RAG\u2019in kurumsal cazibesi b\u00fcy\u00fck \u00f6l\u00e7\u00fcde \u015furadan gelir: yerinde (on-premise) kurulumda model, vekt\u00f6r veritaban\u0131 ve belgelerin tamam\u0131 kurumun kendi sunucular\u0131nda kal\u0131r. \u0130nternet ba\u011flant\u0131s\u0131 olmayan kapal\u0131 a\u011f kurulumlar\u0131 da m\u00fcmk\u00fcnd\u00fcr. KVKK kapsam\u0131ndaki veriler ve gizlilik dereceli belgelerle \u00e7al\u0131\u015fan kurumlar i\u00e7in bu, buluta g\u00f6nderilemeyen veriyi de\u011ferlendirmenin pratikte tek yoludur. Konuyu <a href=\"\/b2b\/kamuda-yapay-zeka\/\">Kamuda Yapay Zeka<\/a> sayfam\u0131zda ayr\u0131nt\u0131l\u0131 ele ald\u0131k.\n<\/div>\n<h2 id=\"sss\">S\u0131k sorulan sorular<\/h2>\n<div class=\"r-faq\">\n<details open>\n<summary>RAG nedir?<\/summary>\n<div class=\"a\">RAG (Retrieval-Augmented Generation \/ eri\u015fim destekli \u00fcretim), b\u00fcy\u00fck dil modelinin bir d\u0131\u015f veri kayna\u011f\u0131na ba\u011flanarak alana \u00f6zg\u00fc ve g\u00fcncel yan\u0131tlar \u00fcretmesini sa\u011flayan yapay zeka tekni\u011fidir. Model, e\u011fitim verisiyle s\u0131n\u0131rl\u0131 kalmak yerine kurumun kendi belgelerinden ilgili bilgiyi bulur ve yan\u0131t\u0131n\u0131 buna dayand\u0131r\u0131r.<\/div>\n<\/details>\n<details>\n<summary>RAG ile modeli yeniden e\u011fitmek (fine-tuning) aras\u0131ndaki fark nedir?<\/summary>\n<div class=\"a\">Fine-tuning modelin a\u011f\u0131rl\u0131klar\u0131n\u0131 de\u011fi\u015ftirir; pahal\u0131, zaman al\u0131c\u0131 ve her veri g\u00fcncellemesinde tekrarlanmas\u0131 gerekir. RAG ise modele dokunmadan d\u0131\u015f veri kayna\u011f\u0131n\u0131 de\u011fi\u015ftirir. Belge ekledi\u011finizde sistem an\u0131nda yeni bilgiyi kullanmaya ba\u015flar. \u00c7o\u011fu kurumsal senaryoda \u00f6nce RAG denenir, gerekirse fine-tuning eklenir.<\/div>\n<\/details>\n<details>\n<summary>RAG hal\u00fcsinasyonu tamamen ortadan kald\u0131r\u0131r m\u0131?<\/summary>\n<div class=\"a\">Tamamen ortadan kald\u0131rmaz ama belirgin \u015fekilde azalt\u0131r. Yan\u0131t kurumun ger\u00e7ek belgelerine dayand\u0131\u011f\u0131 ve kaynak g\u00f6sterilebildi\u011fi i\u00e7in do\u011fruluk ve denetlenebilirlik artar. Y\u00fcksek riskli uygulamalarda \u00e7\u0131kt\u0131 do\u011frulama katman\u0131 (ikinci model ile kontrol, kaynakla kar\u015f\u0131la\u015ft\u0131rma, kay\u0131t tutma) eklenir.<\/div>\n<\/details>\n<details>\n<summary>Anlamsal arama ile anahtar kelime aramas\u0131 aras\u0131ndaki fark nedir?<\/summary>\n<div class=\"a\">Anahtar kelime aramas\u0131 girilen kelimelerin birebir e\u015fle\u015fmesini arar. Anlamsal arama ise sorgunun anlam\u0131n\u0131 ve niyetini kavrar; \u201cd\u00fcztaban i\u00e7in ko\u015fu ayakkab\u0131s\u0131\u201d sorgusuna \u201cdestekli taban\u201d veya \u201cark destekli\u201d i\u00e7erikleri de getirir. K\u0131saca: anahtar kelime aramas\u0131 kelimeyi, anlamsal arama anlam\u0131 arar.<\/div>\n<\/details>\n<details>\n<summary>RAG i\u00e7in hangi donan\u0131m gerekir?<\/summary>\n<div class=\"a\">\u00d6l\u00e7e\u011fe ba\u011fl\u0131d\u0131r. Tek birimlik kavram kan\u0131t\u0131 i\u00e7in 128GB birle\u015fik bellekli bir AI i\u015f istasyonu yeterli olabilir. Kurum \u00e7ap\u0131nda, \u00e7ok kullan\u0131c\u0131l\u0131 sistemlerde \u00e7oklu GPU sunucular tercih edilir. Vekt\u00f6r veritaban\u0131 ve embedding \u00fcretimi de GPU h\u0131zland\u0131rmas\u0131ndan belirgin fayda g\u00f6r\u00fcr.<\/div>\n<\/details>\n<details>\n<summary>Verilerimiz kurum d\u0131\u015f\u0131na \u00e7\u0131kar m\u0131?<\/summary>\n<div class=\"a\">Yerinde (on-premise) kurulumda \u00e7\u0131kmaz. Model, vekt\u00f6r veritaban\u0131 ve belgeler kurumun kendi sunucular\u0131nda \u00e7al\u0131\u015f\u0131r; internet ba\u011flant\u0131s\u0131 olmayan kapal\u0131 a\u011f (air-gap) kurulumlar\u0131 da m\u00fcmk\u00fcnd\u00fcr. Bu, KVKK kapsam\u0131ndaki veriler ve gizlilik dereceli belgeler i\u00e7in kritik bir avantajd\u0131r.<\/div>\n<\/details>\n<details>\n<summary>Hangi veri t\u00fcrleri RAG ile kullan\u0131labilir?<\/summary>\n<div class=\"a\">Metin d\u0131\u015f\u0131nda g\u00f6rsel, ses ve video da kullan\u0131labilir. Bilgisayarl\u0131 g\u00f6r\u00fc ve konu\u015fma i\u015fleme modelleriyle bu i\u00e7erikler de vekt\u00f6re d\u00f6n\u00fc\u015ft\u00fcr\u00fcl\u00fcr; b\u00f6ylece farkl\u0131 veri t\u00fcrleri aras\u0131nda \u00e7apraz arama yap\u0131labilir. \u00c7ok dilli modeller sayesinde T\u00fcrk\u00e7e ve yabanc\u0131 dildeki belgeler birlikte sorgulanabilir.<\/div>\n<\/details>\n<details>\n<summary>Ajan tabanl\u0131 (agentic) RAG nedir?<\/summary>\n<div class=\"a\">Basit RAG tek ge\u00e7i\u015fte arama yap\u0131p yan\u0131t \u00fcretir. Ajan tabanl\u0131 RAG ise birden \u00e7ok yapay zeka ajan\u0131n\u0131n birlikte \u00e7al\u0131\u015ft\u0131\u011f\u0131, \u00e7ok a\u015famal\u0131 arama, ak\u0131l y\u00fcr\u00fctme ve \u00e7\u0131kt\u0131 iyile\u015ftirme d\u00f6ng\u00fcleri i\u00e7eren geli\u015fmi\u015f bir yakla\u015f\u0131md\u0131r. M\u00fc\u015fteri deste\u011fi, hukuki hizmetler ve kurumsal bilgi y\u00f6netimi gibi karma\u015f\u0131k uygulamalarda tercih edilir.<\/div>\n<\/details>\n<\/div>\n<div class=\"r-cta\">\n  <b>Kurumunuz i\u00e7in RAG pilotu kural\u0131m<\/b><\/p>\n<p>Hangi veriyle ba\u015flanaca\u011f\u0131, hangi donan\u0131m\u0131n yetece\u011fi ve nas\u0131l \u00f6l\u00e7\u00fclece\u011fi \u2014 bu \u00fc\u00e7 soruyu birlikte yan\u0131tlay\u0131p size \u00f6zel bir kavram kan\u0131t\u0131 plan\u0131 \u00e7\u0131karal\u0131m.<\/p>\n<div class=\"btns\">\n    <a class=\"b1\" href=\"\/b2b\/iletisim\/\">G\u00f6r\u00fc\u015fme talep et <span aria-hidden=\"true\">\u2192<\/span><\/a><br \/>\n    <a class=\"b2\" href=\"\/b2b\/super-ai-station\/\">AI Station&#8217;\u0131 inceleyin<\/a>\n  <\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>RAG (eri\u015fim destekli \u00fcretim), b\u00fcy\u00fck dil modelinin kurumun kendi verisine dayanarak yan\u0131t \u00fcretmesini sa\u011flar. Nas\u0131l \u00e7al\u0131\u015ft\u0131\u011f\u0131, mimarisi, fine-tuning ile fark\u0131, donan\u0131m gereksinimleri ve kurumunuzda nereden ba\u015flaman\u0131z gerekti\u011fi.<\/p>\n","protected":false},"author":0,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[1],"tags":[],"class_list":["post-12672","post","type-post","status-publish","format-standard","hentry","category-genel"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>RAG Nedir? 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