{"id":393,"date":"2025-11-27T20:00:38","date_gmt":"2025-11-27T17:00:38","guid":{"rendered":"https:\/\/neku.ai\/context-window-nedir-ai\/"},"modified":"2025-11-27T20:01:01","modified_gmt":"2025-11-27T17:01:01","slug":"context-window-nedir-ai","status":"publish","type":"post","link":"https:\/\/neku.ai\/en\/context-window-nedir-ai\/","title":{"rendered":"Context window ile yapay zeka modellerinde ba\u011flam y\u00f6netimi"},"content":{"rendered":"

Context window nedir<\/strong><\/h1>\n

Giri\u015f<\/strong><\/h3>\n

Context window, yapay zeka modellerinde \u00f6zellikle b\u00fcy\u00fck dil modelleri (LLM) i\u00e7inde, sistemin “ba\u011flam\u0131” nas\u0131l hat\u0131rlad\u0131\u011f\u0131n\u0131 ve kulland\u0131\u011f\u0131n\u0131 tan\u0131mlayan temel bir kavramd\u0131r. Basit\u00e7e s\u00f6ylemek gerekirse, modelin ayn\u0131 anda “g\u00f6rebildi\u011fi” veya “hat\u0131rlayabildi\u011fi” metin aral\u0131\u011f\u0131n\u0131 temsil eder. Bu \u00f6zellik, modelin performans\u0131n\u0131, yan\u0131t kalitesini ve i\u015flem kapasitesini do\u011frudan etkiler. Temel AI konular\u0131n\u0131 \u00f6\u011frenen herkes i\u00e7in context window kavram\u0131n\u0131 do\u011fru anlamak kritik \u00f6neme sahiptir.<\/p>\n

\n

Context window nedir tan\u0131m\u0131<\/strong><\/h3>\n

Context window, bir yapay zeka modelinin tahmin s\u0131ras\u0131nda girdi olarak dikkate ald\u0131\u011f\u0131 metin veya veri dizisinin uzunlu\u011funu ifade eder. LLM’lerde bu pencere, ge\u00e7mi\u015f girdilerden ka\u00e7 token\u2019l\u0131k bir k\u0131sm\u0131n modellere tekrar sunulaca\u011f\u0131n\u0131 belirler. \u00d6rne\u011fin 4096 token\u2019l\u0131k bir context window, modelin yaln\u0131zca son 4096 token\u2019\u0131 “hat\u0131rlayabilece\u011fi” anlam\u0131na gelir. Bunun d\u0131\u015f\u0131ndaki bilgiler modelin aktif belle\u011finin d\u0131\u015f\u0131na \u00e7\u0131kar.<\/p>\n

\n

context window nas\u0131l \u00e7al\u0131\u015f\u0131r<\/strong><\/h3>\n

Bir LLM veya metin tabanl\u0131 AI sistemi, gelen her giri\u015f metnini token ad\u0131 verilen k\u00fc\u00e7\u00fck birimlere b\u00f6ler. Bu token dizisi, context window s\u0131n\u0131r\u0131na ula\u015fana kadar i\u015flenir. Model yeni giri\u015fler ald\u0131k\u00e7a eski token\u2019lar pencereden “d\u00fc\u015fer”. Bu s\u00fcrekli hareket, modelin k\u0131sa vadeli ba\u011flam i\u00e7inde mant\u0131k y\u00fcr\u00fctmesini sa\u011flar.<\/p>\n

Temel parametreler ve ayarlar<\/strong><\/h4>\n
    \n
  • Pencere boyutu<\/strong>: Modelin versiyonuna g\u00f6re de\u011fi\u015fir. \u00d6rne\u011fin GPT veya Claude modellerinde 4K, 16K veya 200K token gibi farkl\u0131 boyutlar bulunur. <\/li>\n
  • Token sayac\u0131<\/strong>: Sistem, her sorguda kullan\u0131lan token say\u0131s\u0131n\u0131 izleyerek context limitine yakla\u015f\u0131m\u0131 de\u011ferlendirir. <\/li>\n
  • Dinamik ayarlama<\/strong>: Baz\u0131 uygulamalarda pencere boyutu otomatik optimize edilir veya isteme g\u00f6re k\u0131rp\u0131l\u0131r. <\/li>\n<\/ul>\n

    S\u0131k yap\u0131lan hatalar ve ka\u00e7\u0131nma y\u00f6ntemleri<\/strong><\/h4>\n
      \n
    • \u00c7ok uzun istemler pencereden ta\u015farak veri kayb\u0131na yol a\u00e7abilir. <\/li>\n
    • Gereksiz tekrarlar context maliyetini art\u0131r\u0131r. <\/li>\n
    • Metin \u00f6zetleme veya chunklama ad\u0131mlar\u0131 ile gereksiz veriler azalt\u0131lmal\u0131d\u0131r. <\/li>\n<\/ul>\n

      Ger\u00e7ek sistemlerde uygulama \u00f6rnekleri<\/strong><\/h4>\n

      Kurumsal otomasyon sistemlerinde, \u00f6rne\u011fin n8n ak\u0131\u015flar\u0131 \u00fczerinden bir SAP entegrasyonu yap\u0131l\u0131rken, context window parametresi do\u011fru ayarlanmazsa model eski veri kay\u0131tlar\u0131n\u0131 “unutabilir”. Bu nedenle ak\u0131\u015f i\u00e7inde ba\u011flam g\u00fcncellemeleri ve \u00f6zetleme ad\u0131mlar\u0131 eklenir.<\/p>\n

      \n

      Teknik a\u00e7\u0131klama (derin seviye)<\/strong><\/h3>\n

      Basit bir benzetmeyle context window, bir sohbetin model taraf\u0131ndan ayn\u0131 anda izlenebilen k\u0131sm\u0131d\u0131r. Yeni mesajlar geldik\u00e7e eski konu\u015fmalar geri planda kal\u0131r.
      \nTeknik olarak, model girdi dizisini token baz\u0131nda i\u015fler ve belirlenmi\u015f pencere boyutuna kadar tutar. Bu pencere, hem giri\u015f hem de \u00fcretim token\u2019lar\u0131n\u0131 kapsar.
      \nBeginner seviyesinde bilinmesi gereken nokta \u015fudur: Modelin ba\u011flam\u0131 anlamas\u0131 yaln\u0131zca “g\u00f6z \u00f6n\u00fcnde” olan k\u0131s\u0131m kadard\u0131r. Bu nedenle uzun diyaloglarda \u00f6zetleme, yeniden ba\u011flam verme veya vekt\u00f6r tabanl\u0131 haf\u0131za sistemleri devreye girer.<\/p>\n

      \n

      \u0130\u015fletmeler i\u00e7in neden kritiktir<\/strong><\/h3>\n
        \n
      • Performans<\/strong>: Optimum context window, modelin h\u0131z\u0131n\u0131 ve do\u011frulu\u011funu art\u0131r\u0131r. <\/li>\n
      • G\u00fcvenilirlik<\/strong>: Kritik bilgi kayb\u0131n\u0131 engeller. <\/li>\n
      • Maliyet<\/strong>: Gereksiz token i\u015flemlerini azaltarak API maliyetini d\u00fc\u015f\u00fcr\u00fcr. <\/li>\n
      • \u00d6l\u00e7ekleme<\/strong>: B\u00fcy\u00fck veri setlerinde i\u015flem hacmini y\u00f6netilebilir k\u0131lar. <\/li>\n
      • Otomasyon<\/strong>: n8n gibi ara\u00e7larda i\u015f ak\u0131\u015flar\u0131n\u0131n g\u00fcvenli bir \u015fekilde ba\u011flam korumas\u0131n\u0131 sa\u011flar. <\/li>\n
      • Karar alma<\/strong>: Daha do\u011fru tahmin ve raporlama sonu\u00e7lar\u0131 \u00fcretir. <\/li>\n
      • Operasyonel verimlilik<\/strong>: SAP entegrasyonlar\u0131nda s\u00fcre\u00e7lerin s\u00fcreklili\u011fini destekler. <\/li>\n<\/ul>\n
        \n

        Bu kavram NeKu.AI i\u00e7inde nas\u0131l uygulan\u0131r<\/strong><\/h3>\n

        NeKu.AI\u2019de temel kavram serisinin bir par\u00e7as\u0131 olarak context window, modellerin ger\u00e7ek zamanl\u0131 ba\u011flam y\u00f6netimini optimize etmek i\u00e7in kullan\u0131l\u0131r.
        \nPlatformun entegrasyon katman\u0131nda, her i\u015f ak\u0131\u015f\u0131 bir context s\u0131n\u0131r\u0131 dahilinde \u00e7al\u0131\u015ft\u0131r\u0131l\u0131r. Bu sayede bilgi ta\u015fmas\u0131 veya hatal\u0131 sorgu riski en aza iner.
        \nNeKu.AI\u2019nin mimarisinde, SAP veya CRM sistemlerinden al\u0131nan veriler \u00f6nce \u00f6zetlenir, ard\u0131ndan modelin context window kapasitesine uygun bi\u00e7imde i\u015flenir.<\/p>\n

        \n

        AI geli\u015ftiricileri, \u00fcr\u00fcn y\u00f6neticileri, SAP dan\u0131\u015fmanlar\u0131 i\u00e7in ger\u00e7ek bir senaryo<\/strong><\/h3>\n
          \n
        1. Sorun:<\/strong> Bir LLM tabanl\u0131 chatbot, ERP sisteminden gelen m\u00fc\u015fteri ge\u00e7mi\u015fini uzun oturumlarda unutmaktad\u0131r. <\/li>\n
        2. Ba\u011flam:<\/strong> Chatbot sorgular\u0131 SAP veritaban\u0131 ile n8n i\u015f ak\u0131\u015f\u0131 \u00fczerinden ba\u011flan\u0131r. <\/li>\n
        3. Kavram\u0131n uygulanmas\u0131:<\/strong> Context window s\u0131n\u0131r\u0131 belirlendikten sonra veriler \u00f6zetleme (summarization) algoritmas\u0131yla g\u00fcncellenerek aktif pencerede tutulur. <\/li>\n
        4. Sonu\u00e7:<\/strong> Model art\u0131k \u00f6nceki konu\u015fmalar\u0131n \u00f6zetini koruyarak tutarl\u0131 yan\u0131tlar \u00fcretir. <\/li>\n
        5. \u0130\u015f etkisi:<\/strong> Kullan\u0131c\u0131 memnuniyeti ve do\u011fru yan\u0131t oran\u0131 artar, API maliyeti d\u00fc\u015fer. <\/li>\n<\/ol>\n
          \n

          S\u0131k yap\u0131lan hatalar ve en iyi uygulamalar<\/strong><\/h3>\n
            \n
          • Hata:<\/strong> T\u00fcm ge\u00e7mi\u015f veriyi modele tekrar vermek.
            \n\u00c7\u00f6z\u00fcm:<\/strong> \u00d6nemli ba\u011flamlar\u0131 \u00f6zetleyip pencerede tutmak. <\/li>\n
          • Hata:<\/strong> Token limitini g\u00f6z ard\u0131 etmek.
            \n\u00c7\u00f6z\u00fcm:<\/strong> \u0130stek \u00f6ncesi token tahmin ara\u00e7lar\u0131 kullanmak. <\/li>\n
          • Hata:<\/strong> Konu\u015fma ge\u00e7mi\u015fini rastgele kesmek.
            \n\u00c7\u00f6z\u00fcm:<\/strong> Mant\u0131ksal c\u00fcmle sonlar\u0131nda veya konu b\u00fct\u00fcnl\u00fc\u011f\u00fc korunarak k\u0131rpmak. <\/li>\n
          • En iyi uygulama:<\/strong> B\u00fcy\u00fck sistemlerde context window y\u00f6netimi i\u00e7in bellek vekt\u00f6r veritaban\u0131 veya chunklama tekniklerinden yararlanmak. <\/li>\n<\/ul>\n
            \n

            Sonu\u00e7<\/strong><\/h3>\n

            Context window, yapay zeka ve LLM ekosistemlerinde ba\u011flam y\u00f6netiminin kalbinde yer al\u0131r. Modelin cevap kalitesi, maliyet etkinli\u011fi ve operasyonel do\u011frulu\u011fu bu pencerenin do\u011fru ayarlanmas\u0131na ba\u011fl\u0131d\u0131r.
            \nNeKu.AI\u2019nin i\u00e7erik stratejisinde bu kavram, temel yapay zeka bilgi setinin merkezinde konumlanarak, geli\u015ftiricilerin ve dan\u0131\u015fmanlar\u0131n sa\u011flam yap\u0131lar kurmas\u0131na yard\u0131mc\u0131 olur.<\/p>","protected":false},"excerpt":{"rendered":"

            Context window nedir Giri\u015f Context window, yapay zeka modellerinde \u00f6zellikle b\u00fcy\u00fck dil modelleri (LLM) i\u00e7inde, sistemin “ba\u011flam\u0131” nas\u0131l hat\u0131rlad\u0131\u011f\u0131n\u0131 ve kulland\u0131\u011f\u0131n\u0131 tan\u0131mlayan temel bir kavramd\u0131r. Basit\u00e7e [\u2026]<\/span><\/p>\n","protected":false},"author":2,"featured_media":394,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_sitemap_exclude":false,"_sitemap_priority":"","_sitemap_frequency":"","footnotes":""},"categories":[],"tags":[],"class_list":["post-393","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry"],"yoast_head":"\nContext window ile yapay zeka modellerinde ba\u011flam y\u00f6netimi - NeKu.AI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/neku.ai\/en\/context-window-nedir-ai\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Context window ile yapay zeka modellerinde ba\u011flam y\u00f6netimi - NeKu.AI\" \/>\n<meta property=\"og:description\" content=\"Context window nedir Giri\u015f Context window, yapay zeka modellerinde \u00f6zellikle b\u00fcy\u00fck dil modelleri (LLM) i\u00e7inde, sistemin “ba\u011flam\u0131” nas\u0131l hat\u0131rlad\u0131\u011f\u0131n\u0131 ve kulland\u0131\u011f\u0131n\u0131 tan\u0131mlayan temel bir kavramd\u0131r. 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