{"id":844,"date":"2026-10-27T07:29:33","date_gmt":"2026-10-27T07:29:33","guid":{"rendered":"https:\/\/techparkcyprus.com\/?p=844"},"modified":"2026-10-27T07:29:34","modified_gmt":"2026-10-27T07:29:34","slug":"yapay-zeka-ve-makine-ogrenmesi-yazilim-gelistirmede-yeni-yaklasimlar","status":"publish","type":"post","link":"https:\/\/techparkcyprus.com\/index.php\/2026\/10\/27\/yapay-zeka-ve-makine-ogrenmesi-yazilim-gelistirmede-yeni-yaklasimlar\/","title":{"rendered":"Yapay Zek\u00e2 ve Makine \u00d6\u011frenmesi: Yaz\u0131l\u0131m Geli\u015ftirmede Yeni Yakla\u015f\u0131mlar"},"content":{"rendered":"\n<style>\n#tp-aiml-2026-tr{--tp-ink:#18232e;--tp-muted:#697584;--tp-line:#e7eaf0;--tp-orange:#ff9b00;--tp-orange-soft:#fff6e8;--tp-panel:#f7f8fa;--tp-blue:#17b5d8;--tp-card:#fff;font-family:Roboto,Arial,Helvetica,sans-serif;color:var(--tp-ink);background:#fff;width:100%;line-height:1.74;font-size:16px;overflow-wrap:break-word}\n#tp-aiml-2026-tr *,#tp-aiml-2026-tr *::before,#tp-aiml-2026-tr 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.tp-ver-card{transition:none!important}}\n#tp-aiml-2026-tr .tp-card-note{color:#667280;font-size:12.5px;line-height:1.7;margin:13px 0 0;}\n#tp-aiml-2026-tr .tp-ver-card strong{font-size:24px;white-space:normal;line-height:1.2;letter-spacing:-.025em;}\n#tp-aiml-2026-tr .tp-versions .tp-ver-card{display:flex;flex-direction:column;align-items:flex-start;}\n#tp-aiml-2026-tr .tp-versions .tp-ver-card span{margin-top:10px;}\n#tp-aiml-2026-tr .tp-versions .tp-ver-card p{margin-top:13px;margin-bottom:0;}\n@media(max-width:520px){#tp-aiml-2026-tr .tp-versions .tp-ver-card{display:flex;}#tp-aiml-2026-tr .tp-versions .tp-ver-card strong{font-size:23px;}#tp-aiml-2026-tr .tp-versions .tp-ver-card span{align-self:flex-start;}}\n\n<\/style>\n\n<article id=\"tp-aiml-2026-tr\" lang=\"tr\" aria-label=\"Yapay zek\u00e2 ve makine \u00f6\u011frenmesinin yaz\u0131l\u0131m geli\u015ftirmedeki yeri\">\n <header class=\"tp-hero\">\n  <div class=\"tp-shell tp-hero-grid\">\n   <div>\n    <div class=\"tp-eyebrow\"><span class=\"tp-dot\" aria-hidden=\"true\"><\/span> TECHPARK \/ DEVELOPMENT \/ AI &amp; MACHINE LEARNING<\/div>\n    <h1>Yapay Zek\u00e2 ve Makine \u00d6\u011frenmesi: <span class=\"tp-accent\">Yaz\u0131l\u0131m Geli\u015ftirmede Yeni Yakla\u015f\u0131mlar<\/span><\/h1>\n    <p class=\"tp-deck\">AI, Machine Learning ve \u00fcretken yapay zek\u00e2 kavramlar\u0131n\u0131 netle\u015ftiriyor; web, mobil ve backend projelerinde nas\u0131l kullan\u0131ld\u0131klar\u0131n\u0131, geli\u015ftirici ara\u00e7lar\u0131n\u0131 ve g\u00fcvenilir bir uygulama olu\u015fturman\u0131n temel ad\u0131mlar\u0131n\u0131 inceliyoruz.<\/p>\n    <div class=\"tp-meta\"><span>27 Ekim 2026<\/span><i aria-hidden=\"true\"><\/i><span>Development<\/span><i aria-hidden=\"true\"><\/i><span>Yakla\u015f\u0131k 9 dakika okuma<\/span><\/div>\n   <\/div>\n   <div class=\"tp-hero-visual\" aria-label=\"AI &amp;amp; Machine Learning\">\n    <div class=\"tp-visual-top\"><div style=\"display:flex;align-items:center;gap:12px\"><div class=\"tp-node-mark\" aria-hidden=\"true\">AI<\/div><div><div class=\"tp-mini-eyebrow\">DEVELOPMENT NOTEBOOK<\/div><div class=\"tp-visual-title\">AI &amp; Machine Learning<\/div><\/div><\/div><span class=\"tp-signal\" aria-hidden=\"true\"><\/span><\/div>\n    <div class=\"tp-release-list\"><div class=\"tp-release\"><div><b>Artificial Intelligence<\/b><small>Daha geni\u015f yetenek alan\u0131<\/small><\/div><div class=\"tp-version\">AI <em>KAPSAM<\/em><\/div><\/div><div class=\"tp-release\"><div><b>Machine Learning<\/b><small>Verilerden \u00f6r\u00fcnt\u00fc \u00f6\u011frenme<\/small><\/div><div class=\"tp-version\">ML <em>Y\u00d6NTEM<\/em><\/div><\/div><div class=\"tp-release\"><div><b>Generative AI<\/b><small>Yeni metin ve i\u00e7erik \u00fcretme<\/small><\/div><div class=\"tp-version\">GenAI <em>UYGULAMA<\/em><\/div><\/div><\/div>\n    <div class=\"tp-visual-foot\"><span>Veri \u00b7 model \u00b7 uygulama<\/span><strong>G\u00fcvenilir geli\u015ftirme<\/strong><\/div>\n   <\/div>\n  <\/div>\n <\/header>\n <div class=\"tp-shell tp-article-grid\">\n  <nav class=\"tp-sidebar\" aria-label=\"\u0130\u00c7\u0130NDEK\u0130LER\">\n   <div class=\"tp-sidebar-title\">\u0130\u00c7\u0130NDEK\u0130LER<\/div>\n   <a href=\"#tp-temeller\">AI, ML ve GenAI farklar\u0131<\/a><a href=\"#tp-alanlar\">Yaz\u0131l\u0131mda kullan\u0131m alanlar\u0131<\/a><a href=\"#tp-surec\">ML projesinin geli\u015ftirme s\u00fcreci<\/a><a href=\"#tp-ornek\">Python ile \u00f6rnek uygulama<\/a><a href=\"#tp-rag\">RAG ve yapay zek\u00e2 entegrasyonu<\/a><a href=\"#tp-guvenlik\">G\u00fcvenlik ve kalite<\/a><a href=\"#tp-sss\">S\u0131k sorulan sorular<\/a><a href=\"#tp-kaynaklar\">Resm\u00ee kaynaklar<\/a>\n   <a class=\"tp-source-chip\" href=\"https:\/\/developers.google.com\/machine-learning\/crash-course\" target=\"_blank\" rel=\"noopener noreferrer\"><span class=\"tp-dot\" aria-hidden=\"true\"><\/span> ML \u00f6\u011frenme rehberi<\/a>\n  <\/nav>\n  <div class=\"tp-main\">\n   <div class=\"tp-intro\"><p><strong>K\u0131sa \u00f6zet:<\/strong> Yapay zek\u00e2 (AI), makinelerin ak\u0131ll\u0131 g\u00f6revleri yerine getirmesine y\u00f6nelik geni\u015f bir alan\u0131 ifade eder. <strong>Makine \u00f6\u011frenmesi (ML)<\/strong>, verilerden \u00f6r\u00fcnt\u00fcler \u00f6\u011frenerek tahmin veya s\u0131n\u0131fland\u0131rma yapan sistemler geli\u015ftirmenin y\u00f6ntemlerinden biridir. <strong>\u00dcretken yapay zek\u00e2 (Generative AI)<\/strong> ise metin, g\u00f6r\u00fcnt\u00fc veya kod gibi yeni i\u00e7erikler olu\u015fturabilir. Ba\u015far\u0131l\u0131 bir \u00fcr\u00fcn i\u00e7in yaln\u0131zca model se\u00e7mek de\u011fil, do\u011fru problemi tan\u0131mlamak, veriyi korumak, \u00e7\u0131kt\u0131y\u0131 \u00f6l\u00e7mek ve sistemi izlemek gerekir.<\/p><\/div>\n   <section id=\"tp-temeller\" class=\"tp-section\"><div class=\"tp-kicker\">01 \/ TEMEL KAVRAMLAR<\/div><h2>Yapay zek\u00e2, makine \u00f6\u011frenmesi ve \u00fcretken yapay zek\u00e2 aras\u0131ndaki fark<\/h2><p><strong>AI ile ML ayn\u0131 \u015fey de\u011fildir.<\/strong> Yapay zek\u00e2 bir \u015femsiye kavramd\u0131r; makine \u00f6\u011frenmesi bu \u015femsiyenin alt\u0131nda yer alan bir yakla\u015f\u0131m. \u00dcretken yapay zek\u00e2 uygulamalar\u0131n\u0131n \u00f6nemli bir b\u00f6l\u00fcm\u00fc de makine \u00f6\u011frenmesi modellerine dayan\u0131r, ancak her ML sistemi i\u00e7erik \u00fcretmez. Bir sahtek\u00e2rl\u0131k s\u0131n\u0131fland\u0131r\u0131c\u0131s\u0131, m\u00fc\u015fteri kayb\u0131 tahmini veya arama s\u0131ralama modeli bunun \u00f6rnekleridir.<\/p><p>ML projelerinde <strong>denetimli \u00f6\u011frenme<\/strong> etiketlenmi\u015f \u00f6rneklerden, <strong>denetimsiz \u00f6\u011frenme<\/strong> ise \u00f6nceden verilen hedef etiketler olmadan veri i\u00e7indeki yap\u0131lar\u0131 ke\u015ffetmekten yararlan\u0131r. Hangi y\u00f6ntemin uygun oldu\u011fu \u00e7\u00f6z\u00fclmek istenen probleme ve eldeki veri kalitesine ba\u011fl\u0131d\u0131r.<\/p><div class=\"tp-versions\"><div class=\"tp-ver-card\"><small>KAVRAM<\/small><strong>AI ve problem \u00e7\u00f6zme<\/strong><span>\u00dcst kavram<\/span><p class=\"tp-card-note\">Kurall\u0131 sistemler ve makine \u00f6\u011frenmesi dahil farkl\u0131 yakla\u015f\u0131mlar\u0131 kapsayabilir.<\/p><\/div><div class=\"tp-ver-card\"><small>KAVRAM<\/small><strong>ML ve veriden \u00f6\u011frenme<\/strong><span>AI alt alan\u0131<\/span><p class=\"tp-card-note\">E\u011fitim verilerinden ili\u015fkiler \u00f6\u011frenerek yeni \u00f6rneklerde tahmin \u00fcretir.<\/p><\/div><div class=\"tp-ver-card\"><small>KAVRAM<\/small><strong>GenAI ve \u00fcretim<\/strong><span>\u0130\u00e7erik olu\u015fturma<\/span><p class=\"tp-card-note\">Geni\u015f modeller ve farkl\u0131 mimarilerle metin, kod veya g\u00f6rsel \u00fcretebilir.<\/p><\/div><\/div><div class=\"tp-soft-banner\"><p><strong>Geli\u015ftirme notu:<\/strong> Her soruna yapay zek\u00e2 eklemek do\u011fru yakla\u015f\u0131m de\u011fildir. A\u00e7\u0131k kurallarla g\u00fcvenilir bi\u00e7imde \u00e7\u00f6z\u00fclebilen bir s\u00fcre\u00e7 i\u00e7in klasik yaz\u0131l\u0131m mant\u0131\u011f\u0131 daha basit, hesapl\u0131 ve denetlenebilir olabilir.<\/p><\/div><\/section><section id=\"tp-alanlar\" class=\"tp-section\"><div class=\"tp-kicker\">02 \/ UYGULAMA ALANLARI<\/div><h2>Web, mobil ve backend projelerinde AI nas\u0131l kullan\u0131l\u0131r?<\/h2><p>AI ve ML, mevcut uygulamaya yaln\u0131zca bir sohbet penceresi eklemekten \u00e7ok daha geni\u015f bir potansiyel sunar. En verimli kullan\u0131m alanlar\u0131 genellikle tekrar eden kararlar, geni\u015f veri k\u00fcmeleri veya yo\u011fun i\u00e7erik ak\u0131\u015flar\u0131 etraf\u0131nda \u015fekillenir.<\/p><div class=\"tp-risk-grid\"><div class=\"tp-risk-card\"><span class=\"tp-cve\">WEB &amp; E-T\u0130CARET<\/span><h3>\u00d6neri ve ki\u015fiselle\u015ftirme<\/h3><p>\u00dcr\u00fcn veya i\u00e7erik \u00f6nerileri, arama s\u0131ralamas\u0131 ve kullan\u0131c\u0131 davran\u0131\u015flar\u0131na g\u00f6re uyarlanabilen deneyimler. De\u011ferlendirme: d\u00f6n\u00fc\u015f\u00fcm ve \u00f6neri kalitesi.<\/p><\/div><div class=\"tp-risk-card\"><span class=\"tp-cve\">BACKEND &amp; API<\/span><h3>S\u0131n\u0131fland\u0131rma ve anomali tespiti<\/h3><p>Destek taleplerini konuya g\u00f6re s\u0131n\u0131fland\u0131rma, i\u015flem \u00f6r\u00fcnt\u00fclerini inceleme veya ola\u011fan d\u0131\u015f\u0131 olaylar\u0131 inceleme kuyru\u011funa alma. De\u011ferlendirme: precision, recall ve yanl\u0131\u015f alarm oran\u0131.<\/p><\/div><div class=\"tp-risk-card\"><span class=\"tp-cve\">\u0130\u00c7ER\u0130K &amp; ARAMA<\/span><h3>Kurumsal bilgi asistan\u0131<\/h3><p>Dok\u00fcmantasyon ve izinli \u015firket kay\u0131tlar\u0131 \u00fczerinde arama yaparak yan\u0131t tasla\u011f\u0131 haz\u0131rlama; kaynak g\u00f6sterme ve eri\u015fim kontrolleriyle destekleme.<\/p><\/div><div class=\"tp-risk-card\"><span class=\"tp-cve\">MOB\u0130L &amp; GEL\u0130\u015eT\u0130RME<\/span><h3>G\u00f6r\u00fcnt\u00fc i\u015fleme ve geli\u015ftirici ara\u00e7lar\u0131<\/h3><p>Belge s\u0131n\u0131fland\u0131rma, eri\u015filebilirlik destekleri, kod \u00f6nerileri ve test senaryosu taslaklar\u0131. \u00dcretilen \u00e7\u0131kt\u0131lar mutlaka g\u00f6zden ge\u00e7irilmeli.<\/p><\/div><\/div><p class=\"tp-note\">Bu \u00f6rnekler potansiyel kullan\u0131m alanlar\u0131n\u0131 g\u00f6sterir; performans kazan\u0131m\u0131 veya yat\u0131r\u0131m getirisi, \u00fcr\u00fcn ve veri ko\u015fullar\u0131nda ayr\u0131ca \u00f6l\u00e7\u00fclmelidir.<\/p><\/section><section id=\"tp-surec\" class=\"tp-section\"><div class=\"tp-kicker\">03 \/ ML YA\u015eAM D\u00d6NG\u00dcS\u00dc<\/div><h2>Bir Machine Learning projesi nas\u0131l geli\u015ftirilir?<\/h2><p>Makine \u00f6\u011frenmesi geli\u015ftirme s\u00fcreci yaln\u0131zca e\u011fitim komutundan ibaret de\u011fildir. \u00dcretimde kullan\u0131labilir bir sistem; veri y\u00f6netimi, yaz\u0131l\u0131m testleri, da\u011f\u0131t\u0131m ve izleme a\u015famalar\u0131n\u0131 birlikte ele al\u0131r.<\/p><div class=\"tp-steps\"><div class=\"tp-step\"><div class=\"tp-step-num\">01<\/div><div><h3>Problemi ve ba\u015far\u0131 \u00f6l\u00e7\u00fct\u00fcn\u00fc tan\u0131mlay\u0131n<\/h3><p>\u00d6ncelikle sistemin hangi karar\u0131 iyile\u015ftirece\u011fini belirleyin. Ba\u015far\u0131y\u0131 do\u011fruluk, geri \u00e7a\u011f\u0131rma, gecikme veya kullan\u0131c\u0131ya sa\u011flanan fayda gibi somut g\u00f6stergelerle ifade edin.<\/p><\/div><\/div><div class=\"tp-step\"><div class=\"tp-step-num\">02<\/div><div><h3>Veriyi haz\u0131rlay\u0131n ve yetkileri kontrol edin<\/h3><p>Verinin kayna\u011f\u0131n\u0131, kullan\u0131m hakk\u0131n\u0131, hassasiyetini ve temsil g\u00fcc\u00fcn\u00fc de\u011ferlendirin. E\u011fitim, do\u011frulama ve test verilerini veri s\u0131z\u0131nt\u0131s\u0131n\u0131 \u00f6nleyecek bi\u00e7imde ay\u0131r\u0131n.<\/p><\/div><\/div><div class=\"tp-step\"><div class=\"tp-step-num\">03<\/div><div><h3>Basit bir ba\u015flang\u0131\u00e7 \u00e7\u00f6z\u00fcm\u00fc olu\u015fturun<\/h3><p>\u00d6nce kural tabanl\u0131 yakla\u015f\u0131m\u0131 veya basit bir ML modelini \u00f6l\u00e7\u00fcn. Daha karma\u015f\u0131k modele ger\u00e7ekten ihtiya\u00e7 olup olmad\u0131\u011f\u0131n\u0131 b\u00f6yle anlayabilirsiniz.<\/p><\/div><\/div><div class=\"tp-step\"><div class=\"tp-step-num\">04<\/div><div><h3>Modeli e\u011fitin ve farkl\u0131 \u00f6rneklerle de\u011ferlendirin<\/h3><p>Uygun metrikleri, hata t\u00fcrlerini ve alt gruplardaki performans\u0131 inceleyin. Bir test setinde iyi sonu\u00e7, her ger\u00e7ek kullan\u0131c\u0131 i\u00e7in iyi sonu\u00e7 anlam\u0131na gelmez.<\/p><\/div><\/div><div class=\"tp-step\"><div class=\"tp-step-num\">05<\/div><div><h3>Uygulamaya kontroll\u00fc bi\u00e7imde entegre edin<\/h3><p>API s\u0131n\u0131rlar\u0131n\u0131, \u00f6nbelle\u011fi, zaman a\u015f\u0131m\u0131n\u0131, model s\u00fcr\u00fcm\u00fcn\u00fc, geri d\u00f6n\u00fc\u015f senaryosunu ve maliyet s\u0131n\u0131rlar\u0131n\u0131 belirleyin.<\/p><\/div><\/div><div class=\"tp-step\"><div class=\"tp-step-num\">06<\/div><div><h3>\u00dcretimde izleyin ve yeniden de\u011ferlendirin<\/h3><p>Veri da\u011f\u0131l\u0131m\u0131, hatal\u0131 yan\u0131tlar, gecikme, kullan\u0131c\u0131 geri bildirimi ve i\u015f sonu\u00e7lar\u0131n\u0131 izleyin. Gerekti\u011finde yeniden e\u011fitim veya g\u00fcncelleme uygulay\u0131n.<\/p><\/div><\/div><\/div><\/section><section id=\"tp-ornek\" class=\"tp-section\"><div class=\"tp-kicker\">04 \/ PRAT\u0130K \u00d6RNEK<\/div><h2>Python ile basit bir ML modeli geli\u015ftirelim<\/h2><p>Temel bir s\u0131n\u0131fland\u0131rma ak\u0131\u015f\u0131n\u0131 g\u00f6rmek i\u00e7in <strong>scikit-learn<\/strong> ile gelen Iris veri k\u00fcmesini kullanabiliriz. \u00d6rnek; veriyi ay\u0131r\u0131r, Random Forest modelini e\u011fitir ve ayr\u0131lm\u0131\u015f test verisindeki sonu\u00e7lar\u0131 raporlar.<\/p><div class=\"tp-code\"><div class=\"tp-codebar\"><span>TERMINAL \u00b7 KURULUM<\/span><div class=\"tp-window-dots\" aria-hidden=\"true\"><i><\/i><i><\/i><i><\/i><\/div><\/div><pre><span class=\"tp-command\">python -m pip install scikit-learn<\/span><\/pre><\/div><div class=\"tp-code\"><div class=\"tp-codebar\"><span>PYTHON \u00b7 E\u011e\u0130T\u0130M VE DE\u011eERLEND\u0130RME<\/span><div class=\"tp-window-dots\" aria-hidden=\"true\"><i><\/i><i><\/i><i><\/i><\/div><\/div><pre><span class=\"tp-comment\"># E\u011fitsel \u00f6rnek: Iris veri k\u00fcmesiyle s\u0131n\u0131fland\u0131rma<\/span>\n<span class=\"tp-command\">from sklearn.datasets import load_iris<\/span>\n<span class=\"tp-command\">from sklearn.model_selection import train_test_split<\/span>\n<span class=\"tp-command\">from sklearn.ensemble import RandomForestClassifier<\/span>\n<span class=\"tp-command\">from sklearn.metrics import classification_report<\/span>\n\nX, y = load_iris(return_X_y=True)\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, test_size=0.2, random_state=42, stratify=y\n)\n\nmodel = RandomForestClassifier(n_estimators=100, random_state=42)\nmodel.fit(X_train, y_train)\n\npredictions = model.predict(X_test)\n<span class=\"tp-command\">print(classification_report(y_test, predictions))<\/span><\/pre><\/div><div class=\"tp-soft-banner\"><p><strong>Bu \u00f6rnek e\u011fitim ama\u00e7l\u0131d\u0131r.<\/strong> Iris veri k\u00fcmesi k\u00fc\u00e7\u00fck ve d\u00fczenli bir \u00f6rnektir; ger\u00e7ek projede temsil g\u00fcc\u00fc y\u00fcksek veri, ba\u011f\u0131ms\u0131z test, uygun metrikler, s\u00fcr\u00fcm takibi ve gizlilik kontrolleri gerekir. <code>classification_report<\/code> \u00e7\u0131kt\u0131s\u0131nda precision, recall ve F1-score metrikleri yer al\u0131r.<\/p><\/div><\/section><section id=\"tp-rag\" class=\"tp-section\"><div class=\"tp-kicker\">05 \/ \u00dcR\u00dcNE ENTEGRASYON<\/div><h2>RAG ve LLM tabanl\u0131 uygulamalar nas\u0131l tasarlan\u0131r?<\/h2><p>Bir \u00fcr\u00fcn dok\u00fcmantasyonundan veya bilgi taban\u0131ndan cevap veren yapay zek\u00e2 asistan\u0131nda <strong>RAG (Retrieval-Augmented Generation)<\/strong> yakla\u015f\u0131m\u0131 kullan\u0131labilir. Bu mimaride ilgili belgeler \u00f6nce aran\u0131r; bulunan i\u00e7erik, yan\u0131t olu\u015fturulurken modele ba\u011flam olarak verilir.<\/p><div class=\"tp-steps\"><div class=\"tp-step\"><div class=\"tp-step-num\">01<\/div><div><h3>Kaynaklar\u0131 haz\u0131rlay\u0131n<\/h3><p>Belgeleri temizleyin, par\u00e7alara ay\u0131r\u0131n, s\u00fcr\u00fcmleyin ve her kay\u0131t i\u00e7in eri\u015fim izinlerini saklay\u0131n.<\/p><\/div><\/div><div class=\"tp-step\"><div class=\"tp-step-num\">02<\/div><div><h3>\u0130lgili b\u00f6l\u00fcmleri bulun<\/h3><p>Arama, anahtar s\u00f6zc\u00fckler veya embedding tabanl\u0131 eri\u015fim kullanarak soruyla ilgili belgeleri getirin.<\/p><\/div><\/div><div class=\"tp-step\"><div class=\"tp-step-num\">03<\/div><div><h3>Yan\u0131t\u0131 ba\u011flama dayand\u0131r\u0131n<\/h3><p>Modelden yaln\u0131zca bulunan kaynaklara dayanarak yan\u0131t vermesini, belirsizli\u011fi belirtmesini ve m\u00fcmk\u00fcnse kaynaklar\u0131 g\u00f6stermesini isteyin.<\/p><\/div><\/div><div class=\"tp-step\"><div class=\"tp-step-num\">04<\/div><div><h3>\u0130zinleri ve kaliteyi do\u011frulay\u0131n<\/h3><p>Kaynaklar\u0131 getirmeden \u00f6nce kullan\u0131c\u0131 yetkilerini uygulay\u0131n; yan\u0131tlar\u0131n do\u011frulu\u011funu, gecikmeyi ve hassas bilgi risklerini test edin.<\/p><\/div><\/div><\/div><div class=\"tp-featured-risk\"><div class=\"tp-risk-head\"><span class=\"tp-risk-label\">M\u0130MAR\u0130 NOTU<\/span><span class=\"tp-cve\">RAG \/ LLM<\/span><\/div><h3>RAG hata yapma ihtimalini ortadan kald\u0131rmaz<\/h3><p>Alakas\u0131z veya g\u00fcncel olmayan belgeler al\u0131nd\u0131\u011f\u0131nda model hatal\u0131 yan\u0131t \u00fcretebilir. Kaynak g\u00f6sterme, sonu\u00e7lar\u0131 do\u011frulama ve eri\u015fim denetimini uygulama kodunda zorunlu tutma kritik \u00f6nemdedir.<\/p><\/div><\/section><section id=\"tp-guvenlik\" class=\"tp-section\"><div class=\"tp-kicker\">06 \/ G\u00dcVENL\u0130K VE KAL\u0130TE<\/div><h2>G\u00fcvenilir AI\/ML uygulamalar\u0131 i\u00e7in dikkat edilmesi gerekenler<\/h2><p>\u00dcretken yapay zek\u00e2 uygulamalar\u0131 klasik yaz\u0131l\u0131mlara ek riskler ta\u015f\u0131r. OWASP; <strong>prompt injection<\/strong>, hassas bilgi if\u015fas\u0131, g\u00fcvensiz \u00e7\u0131kt\u0131 kullan\u0131m\u0131 ve a\u015f\u0131r\u0131 yetkili ara\u00e7 eri\u015fimi gibi sorunlar\u0131 \u00f6zellikle vurgular. NIST AI Risk Management Framework ise g\u00fcvenilirli\u011fi sistemin ya\u015fam d\u00f6ng\u00fcs\u00fc boyunca de\u011ferlendirmeyi \u00f6nerir.<\/p><ul class=\"tp-bullets\"><li><span class=\"tp-list-mark\" aria-hidden=\"true\"><\/span><span><strong>Veri gizlili\u011fi:<\/strong> Ki\u015fisel verileri, m\u00fc\u015fteri s\u0131rlar\u0131n\u0131 ve eri\u015fim anahtarlar\u0131n\u0131 gereksiz yere model ba\u011flam\u0131na g\u00f6ndermeyin.<\/span><\/li><li><span class=\"tp-list-mark\" aria-hidden=\"true\"><\/span><span><strong>Girdi ve \u00e7\u0131kt\u0131 kontrolleri:<\/strong> Model yan\u0131t\u0131n\u0131 g\u00fcvenilir komut veya \u00e7al\u0131\u015ft\u0131r\u0131labilir kod kabul etmeyin; HTML, SQL veya shell \u00e7\u0131kt\u0131lar\u0131nda uygun do\u011frulama uygulay\u0131n.<\/span><\/li><li><span class=\"tp-list-mark\" aria-hidden=\"true\"><\/span><span><strong>Yetkilendirme:<\/strong> Kullan\u0131c\u0131n\u0131n izinleri prompt i\u00e7inde de\u011fil, uygulaman\u0131n kendi eri\u015fim kontrol katman\u0131nda uygulanmal\u0131.<\/span><\/li><li><span class=\"tp-list-mark\" aria-hidden=\"true\"><\/span><span><strong>Kalite ve adalet:<\/strong> Verinin \u00f6nyarg\u0131lar\u0131n\u0131, yanl\u0131\u015f pozitifleri, yanl\u0131\u015f negatifleri ve farkl\u0131 kullan\u0131c\u0131 gruplar\u0131na etkilerini de\u011ferlendirin.<\/span><\/li><li><span class=\"tp-list-mark\" aria-hidden=\"true\"><\/span><span><strong>Performans ve maliyet:<\/strong> Yan\u0131t s\u00fcresi, API maliyeti, hata oranlar\u0131 ve istek limitleri i\u00e7in g\u00f6r\u00fcn\u00fcr s\u0131n\u0131rlar olu\u015fturun.<\/span><\/li><li><span class=\"tp-list-mark\" aria-hidden=\"true\"><\/span><span><strong>\u0130nsan denetimi:<\/strong> Finansal, hukuki veya kullan\u0131c\u0131y\u0131 ciddi bi\u00e7imde etkileyen i\u015flemlerde uygun onay ve inceleme ad\u0131mlar\u0131 sa\u011flay\u0131n.<\/span><\/li><\/ul><div class=\"tp-soft-banner\"><p><strong>TechPark Development perspektifi:<\/strong> AI entegrasyonunun ba\u015far\u0131s\u0131, modele verilen komut kadar onu \u00e7evreleyen API tasar\u0131m\u0131na, veri mimarisine, g\u00f6zlemlenebilirli\u011fe ve kullan\u0131c\u0131 deneyimine ba\u011fl\u0131d\u0131r.<\/p><\/div><\/section><section id=\"tp-sss\" class=\"tp-section\"><div class=\"tp-kicker\">07 \/ SIK SORULAN SORULAR<\/div><h2>AI ve Machine Learning hakk\u0131nda merak edilenler<\/h2><div class=\"tp-faq\"><details><summary>AI ile Machine Learning aras\u0131ndaki temel fark nedir?<\/summary><p>AI, ak\u0131ll\u0131 g\u00f6revler ger\u00e7ekle\u015ftiren sistemleri kapsayan geni\u015f bir aland\u0131r. ML, veriden \u00f6\u011frenen modeller olu\u015fturmak i\u00e7in kullan\u0131lan AI yakla\u015f\u0131mlar\u0131ndan biridir.<\/p><\/details><details><summary>Bir web uygulamas\u0131na AI eklemek i\u00e7in s\u0131f\u0131rdan model e\u011fitmek gerekir mi?<\/summary><p>Hay\u0131r. \u0130htiyaca g\u00f6re haz\u0131r bir model veya API kullan\u0131labilir. \u00d6zel e\u011fitim; veri, maliyet, kontrol ve kalite gereksinimlerine g\u00f6re de\u011ferlendirilmelidir.<\/p><\/details><details><summary>AI kullanmak i\u00e7in Python zorunlu mu?<\/summary><p>Hay\u0131r. Python ML ekosisteminde yayg\u0131n olsa da Node.js, Java, C# ve di\u011fer teknolojiler \u00fczerinden model servisleriyle entegrasyon yap\u0131labilir.<\/p><\/details><details><summary>RAG ile fine-tuning ayn\u0131 \u015fey mi?<\/summary><p>Hay\u0131r. RAG d\u0131\u015f kaynaklardan bilgi getirerek modelin yan\u0131t\u0131na ba\u011flam sa\u011flar; fine-tuning ise modelin parametrelerini ek e\u011fitimle de\u011fi\u015ftirir. Ama\u00e7lar\u0131 ve maliyetleri farkl\u0131d\u0131r.<\/p><\/details><details><summary>\u0130lk AI projesine nereden ba\u015flanmal\u0131?<\/summary><p>\u00d6l\u00e7\u00fclebilir, d\u00fc\u015f\u00fck riskli ve dar kapsaml\u0131 bir kullan\u0131m senaryosu se\u00e7in. Basit bir kar\u015f\u0131la\u015ft\u0131rma \u00e7\u00f6z\u00fcm\u00fc olu\u015fturun ve sonu\u00e7lara g\u00f6re kapsam\u0131 geni\u015fletin.<\/p><\/details><\/div><\/section><section id=\"tp-kaynaklar\" class=\"tp-section\"><div class=\"tp-kicker\">08 \/ REFERANSLAR<\/div><h2>Kaynaklar ve ileri okuma<\/h2><p>Bu yaz\u0131, makine \u00f6\u011frenmesi e\u011fitimi, RAG mimarisi ve yapay zek\u00e2 risk y\u00f6netimi hakk\u0131nda yay\u0131mlanm\u0131\u015f teknik kaynaklar\u0131n rehberli\u011finde haz\u0131rlanm\u0131\u015ft\u0131r.<\/p><ul class=\"tp-source-list\"><li>01 \u2014 <a href=\"https:\/\/developers.google.com\/machine-learning\/crash-course\" target=\"_blank\" rel=\"noopener noreferrer\">Machine Learning Crash Course \u2014 Google for Developers<\/a><\/li><li>02 \u2014 <a href=\"https:\/\/cloud.google.com\/use-cases\/retrieval-augmented-generation\" target=\"_blank\" rel=\"noopener noreferrer\">What is Retrieval-Augmented Generation? \u2014 Google Cloud<\/a><\/li><li>03 \u2014 <a href=\"https:\/\/www.nist.gov\/itl\/ai-risk-management-framework\/ai-rmf-development\" target=\"_blank\" rel=\"noopener noreferrer\">AI Risk Management Framework \u2014 NIST<\/a><\/li><li>04 \u2014 <a href=\"https:\/\/genai.owasp.org\/llm-top-10\/\" target=\"_blank\" rel=\"noopener noreferrer\">Top 10 for LLM and Generative AI Applications \u2014 OWASP<\/a><\/li><\/ul><\/section>\n  <\/div>\n <\/div>\n <footer class=\"tp-bottom\"><div class=\"tp-shell\"><div class=\"tp-bottom-card\"><div><small>TECHPARK \/ DEVELOPMENT<\/small><h2>Yapay zek\u00e2 projelerinde de\u011fer, do\u011fru m\u00fchendislikle ortaya \u00e7\u0131kar.<\/h2><p>Ger\u00e7ek bir problemi se\u00e7in, k\u00fc\u00e7\u00fck bir prototiple ba\u015flay\u0131n, \u00f6l\u00e7\u00fclebilir hedefler koyun ve g\u00fcvenli\u011fi geli\u015ftirme s\u00fcrecinin ilk g\u00fcn\u00fcnden itibaren d\u00fc\u015f\u00fcn\u00fcn.<\/p><\/div><a class=\"tp-btn\" href=\"https:\/\/developers.google.com\/machine-learning\/crash-course\" target=\"_blank\" rel=\"noopener noreferrer\">ML \u00f6\u011frenme kaynaklar\u0131n\u0131 incele<\/a><\/div><\/div><\/footer>\n<\/article>\n","protected":false},"excerpt":{"rendered":"<p>TECHPARK \/ DEVELOPMENT \/ AI &amp; MACHINE LEARNING Yapay Zek\u00e2 ve Makine \u00d6\u011frenmesi: Yaz\u0131l\u0131m Geli\u015ftirmede Yeni Yakla\u015f\u0131mlar AI, Machine Learning ve \u00fcretken yapay zek\u00e2 kavramlar\u0131n\u0131 netle\u015ftiriyor; web, mobil ve backend projelerinde nas\u0131l kullan\u0131ld\u0131klar\u0131n\u0131, geli\u015ftirici ara\u00e7lar\u0131n\u0131 ve g\u00fcvenilir bir uygulama olu\u015fturman\u0131n temel ad\u0131mlar\u0131n\u0131 inceliyoruz. 27 Ekim 2026DevelopmentYakla\u015f\u0131k 9 dakika okuma AI DEVELOPMENT NOTEBOOK AI &amp; Machine Learning Artificial IntelligenceDaha geni\u015f yetenek alan\u0131 AI KAPSAM Machine LearningVerilerden \u00f6r\u00fcnt\u00fc \u00f6\u011frenme ML Y\u00d6NTEM Generative AIYeni metin ve i\u00e7erik \u00fcretme GenAI UYGULAMA Veri \u00b7 model \u00b7 uygulamaG\u00fcvenilir geli\u015ftirme \u0130\u00c7\u0130NDEK\u0130LER AI, ML ve GenAI farklar\u0131Yaz\u0131l\u0131mda kullan\u0131m alanlar\u0131ML projesinin geli\u015ftirme s\u00fcreciPython ile \u00f6rnek uygulamaRAG ve yapay zek\u00e2 entegrasyonuG\u00fcvenlik ve kaliteS\u0131k sorulan sorularResm\u00ee kaynaklar ML \u00f6\u011frenme rehberi K\u0131sa \u00f6zet: Yapay zek\u00e2 (AI), makinelerin ak\u0131ll\u0131 g\u00f6revleri yerine getirmesine y\u00f6nelik geni\u015f bir alan\u0131 ifade eder. Makine \u00f6\u011frenmesi (ML), verilerden \u00f6r\u00fcnt\u00fcler \u00f6\u011frenerek tahmin veya s\u0131n\u0131fland\u0131rma yapan sistemler geli\u015ftirmenin y\u00f6ntemlerinden biridir. \u00dcretken yapay zek\u00e2 (Generative AI) ise metin, g\u00f6r\u00fcnt\u00fc veya kod gibi yeni i\u00e7erikler olu\u015fturabilir. Ba\u015far\u0131l\u0131 bir \u00fcr\u00fcn i\u00e7in yaln\u0131zca model se\u00e7mek de\u011fil, do\u011fru problemi tan\u0131mlamak, veriyi korumak, \u00e7\u0131kt\u0131y\u0131 \u00f6l\u00e7mek ve sistemi izlemek gerekir. 01 \/ TEMEL KAVRAMLAR Yapay zek\u00e2, makine \u00f6\u011frenmesi ve \u00fcretken yapay zek\u00e2 aras\u0131ndaki fark AI ile ML ayn\u0131 \u015fey de\u011fildir. Yapay zek\u00e2 bir \u015femsiye kavramd\u0131r; makine \u00f6\u011frenmesi bu \u015femsiyenin alt\u0131nda yer alan bir yakla\u015f\u0131m. \u00dcretken yapay zek\u00e2 uygulamalar\u0131n\u0131n \u00f6nemli bir b\u00f6l\u00fcm\u00fc de makine \u00f6\u011frenmesi modellerine dayan\u0131r, ancak her ML sistemi i\u00e7erik \u00fcretmez. Bir sahtek\u00e2rl\u0131k s\u0131n\u0131fland\u0131r\u0131c\u0131s\u0131, m\u00fc\u015fteri kayb\u0131 tahmini veya arama s\u0131ralama modeli bunun \u00f6rnekleridir. ML projelerinde denetimli \u00f6\u011frenme etiketlenmi\u015f \u00f6rneklerden, denetimsiz \u00f6\u011frenme ise \u00f6nceden verilen hedef etiketler olmadan veri i\u00e7indeki yap\u0131lar\u0131 ke\u015ffetmekten yararlan\u0131r. Hangi y\u00f6ntemin uygun oldu\u011fu \u00e7\u00f6z\u00fclmek istenen probleme ve eldeki veri kalitesine ba\u011fl\u0131d\u0131r. KAVRAMAI ve problem \u00e7\u00f6zme\u00dcst kavram Kurall\u0131 sistemler ve makine \u00f6\u011frenmesi dahil farkl\u0131 yakla\u015f\u0131mlar\u0131 kapsayabilir. KAVRAMML ve veriden \u00f6\u011frenmeAI alt alan\u0131 E\u011fitim verilerinden ili\u015fkiler \u00f6\u011frenerek yeni \u00f6rneklerde tahmin \u00fcretir. KAVRAMGenAI ve \u00fcretim\u0130\u00e7erik olu\u015fturma Geni\u015f modeller ve farkl\u0131 mimarilerle metin, kod veya g\u00f6rsel \u00fcretebilir. Geli\u015ftirme notu: Her soruna yapay zek\u00e2 eklemek do\u011fru yakla\u015f\u0131m de\u011fildir. A\u00e7\u0131k kurallarla g\u00fcvenilir bi\u00e7imde \u00e7\u00f6z\u00fclebilen bir s\u00fcre\u00e7 i\u00e7in klasik yaz\u0131l\u0131m mant\u0131\u011f\u0131 daha basit, hesapl\u0131 ve denetlenebilir olabilir. 02 \/ UYGULAMA ALANLARI Web, mobil ve backend projelerinde AI nas\u0131l kullan\u0131l\u0131r? AI ve ML, mevcut uygulamaya yaln\u0131zca bir sohbet penceresi eklemekten \u00e7ok daha geni\u015f bir potansiyel sunar. En verimli kullan\u0131m alanlar\u0131 genellikle tekrar eden kararlar, geni\u015f veri k\u00fcmeleri veya yo\u011fun i\u00e7erik ak\u0131\u015flar\u0131 etraf\u0131nda \u015fekillenir. WEB &amp; E-T\u0130CARET \u00d6neri ve ki\u015fiselle\u015ftirme \u00dcr\u00fcn veya i\u00e7erik \u00f6nerileri, arama s\u0131ralamas\u0131 ve kullan\u0131c\u0131 davran\u0131\u015flar\u0131na g\u00f6re uyarlanabilen deneyimler. De\u011ferlendirme: d\u00f6n\u00fc\u015f\u00fcm ve \u00f6neri kalitesi. BACKEND &amp; API S\u0131n\u0131fland\u0131rma ve anomali tespiti Destek taleplerini konuya g\u00f6re s\u0131n\u0131fland\u0131rma, i\u015flem \u00f6r\u00fcnt\u00fclerini inceleme veya ola\u011fan d\u0131\u015f\u0131 olaylar\u0131 inceleme kuyru\u011funa alma. De\u011ferlendirme: precision, recall ve yanl\u0131\u015f alarm oran\u0131. \u0130\u00c7ER\u0130K &amp; ARAMA Kurumsal bilgi asistan\u0131 Dok\u00fcmantasyon ve izinli \u015firket kay\u0131tlar\u0131 \u00fczerinde arama yaparak yan\u0131t tasla\u011f\u0131 haz\u0131rlama; kaynak g\u00f6sterme ve eri\u015fim kontrolleriyle destekleme. MOB\u0130L &amp; GEL\u0130\u015eT\u0130RME G\u00f6r\u00fcnt\u00fc i\u015fleme ve geli\u015ftirici ara\u00e7lar\u0131 Belge s\u0131n\u0131fland\u0131rma, eri\u015filebilirlik destekleri, kod \u00f6nerileri ve test senaryosu taslaklar\u0131. \u00dcretilen \u00e7\u0131kt\u0131lar mutlaka g\u00f6zden ge\u00e7irilmeli. Bu \u00f6rnekler potansiyel kullan\u0131m alanlar\u0131n\u0131 g\u00f6sterir; performans kazan\u0131m\u0131 veya yat\u0131r\u0131m getirisi, \u00fcr\u00fcn ve veri ko\u015fullar\u0131nda ayr\u0131ca \u00f6l\u00e7\u00fclmelidir. 03 \/ ML YA\u015eAM D\u00d6NG\u00dcS\u00dc Bir Machine Learning projesi nas\u0131l geli\u015ftirilir? Makine \u00f6\u011frenmesi geli\u015ftirme s\u00fcreci yaln\u0131zca e\u011fitim komutundan ibaret de\u011fildir. \u00dcretimde kullan\u0131labilir bir sistem; veri y\u00f6netimi, yaz\u0131l\u0131m testleri, da\u011f\u0131t\u0131m ve izleme a\u015famalar\u0131n\u0131 birlikte ele al\u0131r. 01 Problemi ve ba\u015far\u0131 \u00f6l\u00e7\u00fct\u00fcn\u00fc tan\u0131mlay\u0131n \u00d6ncelikle sistemin hangi karar\u0131 iyile\u015ftirece\u011fini belirleyin. Ba\u015far\u0131y\u0131 do\u011fruluk, geri \u00e7a\u011f\u0131rma, gecikme veya kullan\u0131c\u0131ya sa\u011flanan fayda gibi somut g\u00f6stergelerle ifade edin. 02 Veriyi haz\u0131rlay\u0131n ve yetkileri kontrol edin Verinin kayna\u011f\u0131n\u0131, kullan\u0131m hakk\u0131n\u0131, hassasiyetini ve temsil g\u00fcc\u00fcn\u00fc de\u011ferlendirin. E\u011fitim, do\u011frulama ve test verilerini veri s\u0131z\u0131nt\u0131s\u0131n\u0131 \u00f6nleyecek bi\u00e7imde ay\u0131r\u0131n. 03 Basit bir ba\u015flang\u0131\u00e7 \u00e7\u00f6z\u00fcm\u00fc olu\u015fturun \u00d6nce kural tabanl\u0131 yakla\u015f\u0131m\u0131 veya basit bir ML modelini \u00f6l\u00e7\u00fcn. Daha karma\u015f\u0131k modele ger\u00e7ekten ihtiya\u00e7 olup olmad\u0131\u011f\u0131n\u0131 b\u00f6yle anlayabilirsiniz. 04 Modeli e\u011fitin ve farkl\u0131 \u00f6rneklerle de\u011ferlendirin Uygun metrikleri, hata t\u00fcrlerini ve alt gruplardaki performans\u0131 inceleyin. Bir test setinde iyi sonu\u00e7, her ger\u00e7ek kullan\u0131c\u0131 i\u00e7in iyi sonu\u00e7 anlam\u0131na gelmez. 05 Uygulamaya kontroll\u00fc bi\u00e7imde entegre edin API s\u0131n\u0131rlar\u0131n\u0131, \u00f6nbelle\u011fi, zaman a\u015f\u0131m\u0131n\u0131, model s\u00fcr\u00fcm\u00fcn\u00fc, geri d\u00f6n\u00fc\u015f senaryosunu ve maliyet s\u0131n\u0131rlar\u0131n\u0131 belirleyin. 06 \u00dcretimde izleyin ve yeniden de\u011ferlendirin Veri da\u011f\u0131l\u0131m\u0131, hatal\u0131 yan\u0131tlar, gecikme, kullan\u0131c\u0131 geri bildirimi ve i\u015f sonu\u00e7lar\u0131n\u0131 izleyin. Gerekti\u011finde yeniden e\u011fitim veya g\u00fcncelleme uygulay\u0131n. 04 \/ PRAT\u0130K \u00d6RNEK Python ile basit bir ML modeli geli\u015ftirelim Temel bir s\u0131n\u0131fland\u0131rma ak\u0131\u015f\u0131n\u0131 g\u00f6rmek i\u00e7in scikit-learn ile gelen Iris veri k\u00fcmesini kullanabiliriz. \u00d6rnek; veriyi ay\u0131r\u0131r, Random Forest modelini e\u011fitir ve ayr\u0131lm\u0131\u015f test verisindeki sonu\u00e7lar\u0131 raporlar. TERMINAL \u00b7 KURULUM python -m pip install scikit-learn PYTHON \u00b7 E\u011e\u0130T\u0130M VE DE\u011eERLEND\u0130RME # E\u011fitsel \u00f6rnek: Iris veri k\u00fcmesiyle s\u0131n\u0131fland\u0131rma from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import classification_report X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42, stratify=y ) model = RandomForestClassifier(n_estimators=100, random_state=42) model.fit(X_train, y_train) predictions = model.predict(X_test) print(classification_report(y_test, predictions)) Bu \u00f6rnek e\u011fitim ama\u00e7l\u0131d\u0131r. Iris veri k\u00fcmesi k\u00fc\u00e7\u00fck ve d\u00fczenli bir \u00f6rnektir; ger\u00e7ek projede temsil g\u00fcc\u00fc y\u00fcksek veri, ba\u011f\u0131ms\u0131z test, uygun metrikler, s\u00fcr\u00fcm takibi ve gizlilik kontrolleri gerekir. classification_report \u00e7\u0131kt\u0131s\u0131nda precision, recall ve F1-score metrikleri yer al\u0131r. 05 \/ \u00dcR\u00dcNE ENTEGRASYON RAG ve LLM tabanl\u0131 uygulamalar nas\u0131l tasarlan\u0131r? Bir \u00fcr\u00fcn dok\u00fcmantasyonundan veya bilgi taban\u0131ndan cevap veren yapay zek\u00e2 asistan\u0131nda RAG (Retrieval-Augmented Generation) yakla\u015f\u0131m\u0131 kullan\u0131labilir. Bu mimaride ilgili belgeler \u00f6nce aran\u0131r; bulunan i\u00e7erik, yan\u0131t olu\u015fturulurken modele ba\u011flam olarak verilir. 01 Kaynaklar\u0131 haz\u0131rlay\u0131n Belgeleri temizleyin, par\u00e7alara ay\u0131r\u0131n, s\u00fcr\u00fcmleyin ve her kay\u0131t i\u00e7in eri\u015fim izinlerini saklay\u0131n. 02 \u0130lgili b\u00f6l\u00fcmleri bulun Arama, anahtar s\u00f6zc\u00fckler veya embedding tabanl\u0131 eri\u015fim kullanarak soruyla ilgili belgeleri getirin. 03 Yan\u0131t\u0131 ba\u011flama dayand\u0131r\u0131n Modelden yaln\u0131zca bulunan kaynaklara dayanarak yan\u0131t vermesini, belirsizli\u011fi belirtmesini ve m\u00fcmk\u00fcnse kaynaklar\u0131 g\u00f6stermesini isteyin. 04 \u0130zinleri ve kaliteyi do\u011frulay\u0131n Kaynaklar\u0131 getirmeden \u00f6nce kullan\u0131c\u0131 yetkilerini uygulay\u0131n; yan\u0131tlar\u0131n do\u011frulu\u011funu, gecikmeyi ve hassas bilgi risklerini test edin. M\u0130MAR\u0130 NOTURAG \/ LLM RAG hata yapma ihtimalini ortadan kald\u0131rmaz Alakas\u0131z veya g\u00fcncel olmayan belgeler al\u0131nd\u0131\u011f\u0131nda model hatal\u0131 yan\u0131t \u00fcretebilir. Kaynak g\u00f6sterme, sonu\u00e7lar\u0131 do\u011frulama ve eri\u015fim denetimini uygulama kodunda zorunlu tutma kritik \u00f6nemdedir. 06 \/ G\u00dcVENL\u0130K VE KAL\u0130TE G\u00fcvenilir AI\/ML uygulamalar\u0131 i\u00e7in dikkat edilmesi gerekenler \u00dcretken yapay zek\u00e2 uygulamalar\u0131 klasik yaz\u0131l\u0131mlara ek riskler ta\u015f\u0131r. OWASP; prompt injection, hassas bilgi if\u015fas\u0131, g\u00fcvensiz \u00e7\u0131kt\u0131 kullan\u0131m\u0131 ve a\u015f\u0131r\u0131 yetkili ara\u00e7 eri\u015fimi gibi sorunlar\u0131 \u00f6zellikle<\/p>\n","protected":false},"author":2,"featured_media":845,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[22,11],"tags":[],"class_list":["post-844","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-machine-learning","category-development"],"jetpack_sharing_enabled":true,"jetpack_featured_media_url":"https:\/\/techparkcyprus.com\/wp-content\/uploads\/2026\/10\/Yapay-Zeka-ve-Makine-Ogrenmesi-1.png","_links":{"self":[{"href":"https:\/\/techparkcyprus.com\/index.php\/wp-json\/wp\/v2\/posts\/844","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/techparkcyprus.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/techparkcyprus.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/techparkcyprus.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/techparkcyprus.com\/index.php\/wp-json\/wp\/v2\/comments?post=844"}],"version-history":[{"count":1,"href":"https:\/\/techparkcyprus.com\/index.php\/wp-json\/wp\/v2\/posts\/844\/revisions"}],"predecessor-version":[{"id":846,"href":"https:\/\/techparkcyprus.com\/index.php\/wp-json\/wp\/v2\/posts\/844\/revisions\/846"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/techparkcyprus.com\/index.php\/wp-json\/wp\/v2\/media\/845"}],"wp:attachment":[{"href":"https:\/\/techparkcyprus.com\/index.php\/wp-json\/wp\/v2\/media?parent=844"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/techparkcyprus.com\/index.php\/wp-json\/wp\/v2\/categories?post=844"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/techparkcyprus.com\/index.php\/wp-json\/wp\/v2\/tags?post=844"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}