{"id":3114,"date":"2026-08-10T12:21:42","date_gmt":"2026-08-10T09:21:42","guid":{"rendered":"https:\/\/shareai.now\/?p=3114"},"modified":"2026-08-11T03:20:29","modified_gmt":"2026-08-11T00:20:29","slug":"llmler-icin-token-sikistirma","status":"publish","type":"post","link":"https:\/\/shareai.now\/tr\/blog\/icgoruler\/llmler-icin-token-sikistirma\/","title":{"rendered":"LLM'ler i\u00e7in Token S\u0131k\u0131\u015ft\u0131rma: Y\u00f6nlendirme \u00d6ncesi Ba\u011flam Maliyetini Azalt\u0131n"},"content":{"rendered":"

LLM'ler i\u00e7in token s\u0131k\u0131\u015ft\u0131rma<\/strong> modellerle bulu\u015fmadan \u00f6nce istemleri, al\u0131nan ba\u011flam\u0131, ara\u00e7 \u00e7\u0131kt\u0131lar\u0131, sohbet ge\u00e7mi\u015fini ve g\u00fcnl\u00fckleri k\u00fc\u00e7\u00fcltme uygulamas\u0131d\u0131r. Y\u00f6nlendirme, de\u011ferlendirme veya yedekleme i\u015flemlerinin yerine ge\u00e7mez. Bu sistemlerin daha temiz girdilerle \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flar.<\/p>\n\n\n\n

Bu \u00f6nemlidir \u00e7\u00fcnk\u00fc \u00e7o\u011fu AI maliyet ve gecikme sorunlar\u0131, iste\u011finiz uygulaman\u0131zdan \u00e7\u0131kmadan \u00f6nce ba\u015flar. Bir destek botu, yaln\u0131zca \u00fc\u00e7 bilgi \u00f6nemli oldu\u011funda t\u00fcm bilet dizisini g\u00f6nderebilir. Bir ajan, yaln\u0131zca durum, miktar ve sonraki ad\u0131m\u0131 gerekti\u011finde tam ara\u00e7 yan\u0131t\u0131n\u0131 yap\u0131\u015ft\u0131rabilir. Bir RAG i\u015f ak\u0131\u015f\u0131, bir kompakt yan\u0131t yeterli oldu\u011funda be\u015f par\u00e7a alabilir.<\/p>\n\n\n\n

OpenAI a\u00e7\u0131kl\u0131yor<\/a> API kullan\u0131m\u0131n\u0131n tokenlar \u00fczerinden \u00f6l\u00e7\u00fcld\u00fc\u011f\u00fcn\u00fc ve bu tokenlar\u0131n hem giri\u015f hem de \u00e7\u0131k\u0131\u015f metninden geldi\u011fini. Uzun ba\u011flam, \u00fccretsiz ba\u011flam de\u011fildir. Ama\u00e7 modeli a\u00e7 b\u0131rakmak de\u011fil. Ama\u00e7, modelin ihtiya\u00e7 duydu\u011fu karar, kan\u0131t ve k\u0131s\u0131tlamalar\u0131 koruyan en k\u00fc\u00e7\u00fck ba\u011flam\u0131 g\u00f6ndermektir.<\/p>\n\n\n\n

Y\u00f6nlendirme \u00f6ncesinde token s\u0131k\u0131\u015ft\u0131rman\u0131n \u00f6nemi<\/h2>\n\n\n\n

Bir\u00e7ok ekip, maliyet optimizasyonunu bir model se\u00e7imi sorunu olarak d\u00fc\u015f\u00fcn\u00fcr: kolay i\u015fleri daha ucuz bir modele g\u00f6nderin, zor i\u015fler i\u00e7in premium modelleri ay\u0131r\u0131n ve bir sa\u011flay\u0131c\u0131 rotas\u0131 bozuldu\u011funda yedekleme kullan\u0131n. Bu faydal\u0131d\u0131r, ancak temel bir noktay\u0131 ka\u00e7\u0131r\u0131r: y\u00f6nlendirici yaln\u0131zca ona verdi\u011finiz iste\u011fi g\u00f6r\u00fcr.<\/p>\n\n\n\n

E\u011fer istek \u015fi\u015firilmi\u015fse, her a\u015fa\u011f\u0131 ak\u0131\u015f karar\u0131 daha zor hale gelir. Ucuz bir model, \u00e7ok fazla g\u00fcr\u00fclt\u00fc ald\u0131\u011f\u0131 i\u00e7in ba\u015far\u0131s\u0131z olabilir. Bir s\u0131n\u0131r modeli, istem karma\u015f\u0131k oldu\u011fu i\u00e7in gerekli g\u00f6r\u00fcnebilir. G\u00f6zlemlenebilirlik y\u00fcksek harcama g\u00f6sterebilir, ancak buna neden olan \u00f6nlenebilir ba\u011flam\u0131 g\u00f6stermez.<\/p>\n\n\n\n

S\u0131k\u0131\u015ft\u0131rma, model eri\u015fiminden \u00f6nce bir ad\u0131m ekler: y\u00fck\u00fc azalt\u0131n, niyeti koruyun, ard\u0131ndan y\u00f6nlendirin. ShareAI\u2019nin model pazar\u0131<\/a>, bu daha temiz istek daha sonra model se\u00e7imi, fiyat, gecikme, kullan\u0131labilirlik ve y\u00f6nlendirme ihtiya\u00e7lar\u0131na kar\u015f\u0131 bir API \u00fczerinden de\u011ferlendirilebilir.<\/p>\n\n\n\n

Ne s\u0131k\u0131\u015ft\u0131r\u0131lmal\u0131?<\/h2>\n\n\n\n

Her token ayn\u0131 muameleyi hak etmez. Baz\u0131 metinler talimat a\u00e7\u0131s\u0131ndan kritiktir. Baz\u0131 metinler kan\u0131tt\u0131r. Baz\u0131 metinler \u00f6nceki ad\u0131mlardan kalan kal\u0131nt\u0131d\u0131r.<\/p>\n\n\n\n

Giri\u015f alan\u0131<\/th>S\u0131k\u0131\u015ft\u0131rma yakla\u015f\u0131m\u0131<\/th>Ne korunmal\u0131<\/th><\/tr><\/thead>
Sohbet ge\u00e7mi\u015fi<\/td>Eski d\u00f6n\u00fc\u015fleri duruma, kararlara, k\u0131s\u0131tlamalara ve a\u00e7\u0131k sorulara \u00f6zetle.<\/td>Kullan\u0131c\u0131 niyeti, taahh\u00fctler, isimler, tercihler ve \u00e7\u00f6z\u00fclmemi\u015f g\u00f6revler.<\/td><\/tr>
RAG par\u00e7alar\u0131<\/td>Dar bir \u015fekilde al, yinelenenleri kald\u0131r ve mevcut soruyu yan\u0131tlayan pasajlar\u0131 \u00e7\u0131kar.<\/td>Al\u0131nt\u0131lar, kesin ger\u00e7ekler, \u00e7eli\u015fkili kan\u0131tlar ve g\u00fcncellik sinyalleri.<\/td><\/tr>
Ara\u00e7 \u00e7\u0131kt\u0131lar\u0131<\/td>Ayr\u0131nt\u0131l\u0131 yan\u0131tlar\u0131 kompakt yap\u0131land\u0131r\u0131lm\u0131\u015f alanlara d\u00f6n\u00fc\u015ft\u00fcr.<\/td>Durum, kimlikler, miktarlar, hatalar, zaman damgalar\u0131 ve sonraki eylemler.<\/td><\/tr>
G\u00fcnl\u00fckler ve izler<\/td>Tekrarlanan olaylar\u0131 grupla ve yaln\u0131zca anomaliyi, say\u0131y\u0131 ve ilgili \u00f6rne\u011fi tut.<\/td>Hata deseni, s\u0131kl\u0131k, etkilenen hizmet ve zaman \u00e7izelgesi.<\/td><\/tr>
Sistem talimatlar\u0131<\/td>Yinelenen politika metnini kald\u0131r ve sabit talimatlar\u0131 g\u00f6revle ilgili ba\u011flamdan ay\u0131r.<\/td>G\u00fcvenlik kurallar\u0131, \u00e7\u0131kt\u0131 s\u00f6zle\u015fmesi, rol k\u0131s\u0131tlamalar\u0131 ve ara\u00e7 izinleri.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Be\u015f pratik s\u0131k\u0131\u015ft\u0131rma y\u00f6ntemi<\/h2>\n\n\n\n

1. Durumu \u00f6zetleyin, d\u00fczyaz\u0131y\u0131 de\u011fil<\/h3>\n\n\n\n

Zay\u0131f bir \u00f6zet, uzun bir konu\u015fmay\u0131 daha k\u0131sa bir paragrafa yeniden yazar. Faydal\u0131 bir \u00f6zet, operasyonel durumu korur: kullan\u0131c\u0131n\u0131n ne istedi\u011fi, neyin zaten denendi\u011fi, neyin ba\u015far\u0131s\u0131z oldu\u011fu, hangi k\u0131s\u0131tlamalar\u0131n kald\u0131\u011f\u0131 ve bir sonraki karar\u0131n ne oldu\u011fu.<\/p>\n\n\n\n

Ajanlar i\u00e7in, durum \u00f6zetleri bilinen s\u0131n\u0131r noktalar\u0131nda yenilenmelidir: bir ara\u00e7 \u00e7a\u011fr\u0131s\u0131ndan sonra, bir kullan\u0131c\u0131 karar\u0131ndan sonra, bir i\u015f ak\u0131\u015f\u0131 ad\u0131m\u0131ndan sonra veya modelleri de\u011fi\u015ftirmeden \u00f6nce. Kimlikleri, gereksinimleri veya olumsuz k\u0131s\u0131tlamalar\u0131 s\u0131k\u0131\u015ft\u0131rmay\u0131n.<\/p>\n\n\n\n

2. Ara\u00e7 \u00e7\u0131kt\u0131lar\u0131ndan alanlar \u00e7\u0131kar\u0131n<\/h3>\n\n\n\n

Bir\u00e7ok ara\u00e7 \u00e7a\u011fr\u0131s\u0131, bir sonraki model ad\u0131m\u0131n\u0131n ihtiya\u00e7 duydu\u011fundan \u00e7ok daha fazla metin d\u00f6nd\u00fcr\u00fcr. T\u00fcm yan\u0131t\u0131 iletmek yerine, \u00f6nemli olan alanlar\u0131 \u00e7\u0131kar\u0131n. Bir \u00f6deme sorgulamas\u0131 m\u00fc\u015fteri kimli\u011fi, fatura durumu, bakiye, son \u00f6deme tarihi ve risk bayraklar\u0131 haline gelebilir. Bir arama sonucu ba\u015fl\u0131k, kanonik URL, tarih ve iddiay\u0131 destekleyen bir c\u00fcmle haline gelebilir.<\/p>\n\n\n\n

3. \u00dcretimden \u00f6nce al\u0131m\u0131 filtreleyin<\/h3>\n\n\n\n

RAG sistemleri genellikle benzer par\u00e7alar\u0131, eski par\u00e7alar\u0131 veya anahtar kelimelerle e\u015fle\u015fen ancak niyeti kar\u015f\u0131lamayan par\u00e7alar\u0131 g\u00f6ndererek jetonlar\u0131 bo\u015fa harcar. Bir s\u0131k\u0131\u015ft\u0131rma katman\u0131, \u00f6rt\u00fc\u015fen pasajlar\u0131 yinelenebilir, eski ba\u011flam\u0131 kald\u0131rabilir ve yaln\u0131zca mevcut sorguyu yan\u0131tlayan kan\u0131tlar\u0131 tutabilir.<\/p>\n\n\n\n

Nihai yan\u0131t\u0131n al\u0131nt\u0131lar gerektirdi\u011fi durumlarda bu \u00f6zellikle \u00f6nemlidir. Ba\u011flam\u0131 s\u0131k\u0131\u015ft\u0131r\u0131n, ancak yan\u0131t\u0131 daha sonra do\u011frulamak i\u00e7in yeterli kaynak ayr\u0131nt\u0131s\u0131n\u0131 koruyun.<\/p>\n\n\n\n

4. Yap\u0131land\u0131r\u0131lm\u0131\u015f ara \u00e7\u0131kt\u0131lar kullan\u0131n<\/h3>\n\n\n\n

Serbest bi\u00e7imli ara metin h\u0131zla b\u00fcy\u00fcr. Yap\u0131land\u0131r\u0131lm\u0131\u015f \u00e7\u0131kt\u0131lar daha k\u00fc\u00e7\u00fck ve denetlenmesi daha kolay kal\u0131r. Her aday eylemi a\u00e7\u0131klamak i\u00e7in bir model istemek yerine, eylem, g\u00fcven, neden, engelleyici sorun ve gerekli giri\u015f gibi alanlarla kompakt bir se\u00e7enek listesi d\u00f6nd\u00fcrmesini isteyin.<\/p>\n\n\n\n

5. \u0130stek \u00f6nbelle\u011fini ayr\u0131 bir kald\u0131ra\u00e7 olarak ele al\u0131n<\/h3>\n\n\n\n

\u0130stek \u00f6nbelle\u011fi, desteklenen sistemlerde tekrarlanan \u00f6n eklerin maliyetini veya gecikmesini azaltabilir, ancak jeton s\u0131k\u0131\u015ft\u0131rma ile ayn\u0131 \u015fey de\u011fildir. \u00d6nbelle\u011fe al\u0131nm\u0131\u015f metin yine de ba\u011flam penceresi alan\u0131n\u0131 t\u00fcketebilir ve yine de istekleri incelemeyi zorla\u015ft\u0131rabilir. Anthropic\u2019in ba\u011flam-penceresi<\/a> ve istek-\u00f6nbelle\u011fi<\/a> belgeler, \u00f6nbellekleme ve ba\u011flam tasar\u0131m\u0131n\u0131n ili\u015fkili ancak farkl\u0131 sorunlar\u0131 \u00e7\u00f6zd\u00fc\u011f\u00fcn\u00fc hat\u0131rlat\u0131c\u0131 olarak faydal\u0131d\u0131r.<\/p>\n\n\n\n

S\u0131k\u0131\u015ft\u0131rman\u0131n bir ShareAI i\u015f ak\u0131\u015f\u0131na nas\u0131l uydu\u011fu<\/h2>\n\n\n\n

ShareAI, bir uygulamay\u0131 kendinizin olu\u015fturdu\u011fu bir yer de\u011fil, bir AI pazar\u0131 ve API'dir. Uygulaman\u0131z kullan\u0131c\u0131 deneyimini, i\u015f ak\u0131\u015f\u0131 mant\u0131\u011f\u0131n\u0131, ba\u011flam se\u00e7imini ve s\u0131k\u0131\u015ft\u0131rma ad\u0131m\u0131n\u0131 y\u00f6netir. ShareAI, model eri\u015fim taraf\u0131nda yard\u0131mc\u0131 olur: 150+ model i\u00e7in tek bir API, pazar g\u00f6r\u00fcn\u00fcrl\u00fc\u011f\u00fc, y\u00f6nlendirme, yedekleme ve kullan\u0131m takibi.<\/p>\n\n\n\n

  1. Ham kullan\u0131c\u0131 iste\u011fini ve uygulama ba\u011flam\u0131n\u0131 toplay\u0131n.<\/li>
  2. \u00c7iftleri, eski ba\u011flam\u0131 ve alakas\u0131z geri alma sonu\u00e7lar\u0131n\u0131 kald\u0131r\u0131n.<\/li>
  3. Daha eski konu\u015fma durumunu ve ayr\u0131nt\u0131l\u0131 ara\u00e7 \u00e7\u0131kt\u0131s\u0131n\u0131 s\u0131k\u0131\u015ft\u0131r\u0131n.<\/li>
  4. Temizlenmi\u015f iste\u011fi g\u00f6nderin ShareAI API<\/a>.<\/li>
  5. Model uyumu, fiyat, gecikme, kullan\u0131labilirlik ve yedekleme ihtiya\u00e7lar\u0131na g\u00f6re y\u00f6nlendirin.<\/li>
  6. Yan\u0131t sonras\u0131 kaliteyi, maliyeti ve hata kal\u0131plar\u0131n\u0131 \u00f6l\u00e7\u00fcn.<\/li><\/ol>\n\n\n\n

    Yap\u0131c\u0131lar i\u00e7in s\u0131k\u0131\u015ft\u0131rma, para kazanmay\u0131 da daha temiz hale getirebilir. Mevcut bir uygulama AI \u00e7\u0131kar\u0131m trafi\u011fini ShareAI \u00fczerinden y\u00f6nlendiriyorsa, Yap\u0131c\u0131 bir ek \u00fccret veya marj yap\u0131land\u0131rabilir ve olu\u015fturulan kullan\u0131m temelinde ayl\u0131k \u00f6demeler alabilir. Daha temiz ba\u011flam, y\u00f6nlendirilen kullan\u0131m\u0131 m\u00fc\u015fterilere a\u00e7\u0131klamay\u0131 kolayla\u015ft\u0131r\u0131r \u00e7\u00fcnk\u00fc yo\u011fun kullan\u0131c\u0131lar ger\u00e7ekten olu\u015fturduklar\u0131 AI trafi\u011fi i\u00e7in \u00f6deme yapar.<\/p>\n\n\n\n

    S\u0131k\u0131\u015ft\u0131rman\u0131n i\u015fe yaray\u0131p yaramad\u0131\u011f\u0131n\u0131 nas\u0131l \u00f6l\u00e7ersiniz<\/h2>\n\n\n\n

    S\u0131k\u0131\u015ft\u0131rma yaln\u0131zca kalite korunursa faydal\u0131d\u0131r. Bunu \u00fcretim de\u011fi\u015fikli\u011fi gibi takip edin, ak\u0131ll\u0131 bir istem hilesi gibi de\u011fil.<\/p>\n\n\n\n

    • \u0130stek ba\u015f\u0131na giri\u015f tokenlar\u0131:<\/strong> hedeflenen i\u015f ak\u0131\u015flar\u0131 i\u00e7in azalmal\u0131d\u0131r.<\/li>
    • \u00c7\u0131k\u0131\u015f kalitesi:<\/strong> temsilci g\u00f6revlerde sabit kalmal\u0131d\u0131r.<\/li>
    • Geri d\u00f6n\u00fc\u015f oran\u0131:<\/strong> daha ucuz yollar daha zay\u0131f ba\u011flam ald\u0131\u011f\u0131 i\u00e7in artmamal\u0131d\u0131r.<\/li>
    • Gecikme:<\/strong> iyile\u015fmeli veya en az\u0131ndan herhangi bir \u00f6n i\u015fleme ad\u0131m\u0131n\u0131 hakl\u0131 \u00e7\u0131karmal\u0131d\u0131r.<\/li>
    • Y\u00fckselme oran\u0131:<\/strong> s\u0131k\u0131\u015ft\u0131r\u0131lm\u0131\u015f ba\u011flam kullan\u0131c\u0131lar\u0131 veya ajanlar\u0131 tekrar sormaya zorlad\u0131\u011f\u0131nda bunu g\u00f6stermelidir.<\/li>
    • Ba\u015far\u0131l\u0131 g\u00f6rev ba\u015f\u0131na maliyet:<\/strong> sadece talep ba\u015f\u0131na maliyet de\u011fil, d\u00fc\u015fmelidir.<\/li><\/ul>\n\n\n\n

      \u0130yi bir test seti k\u0131sa istemler, uzun istemler, ara\u00e7 a\u011f\u0131rl\u0131kl\u0131 ajan g\u00f6revleri, RAG sorular\u0131 ve eksik ba\u011flam\u0131n yanl\u0131\u015f bir cevaba neden olaca\u011f\u0131 u\u00e7 durumlar\u0131 i\u00e7erir. S\u0131k\u0131\u015ft\u0131r\u0131lm\u0131\u015f ve s\u0131k\u0131\u015ft\u0131r\u0131lmam\u0131\u015f \u00e7al\u0131\u015fmalar\u0131 kar\u015f\u0131la\u015ft\u0131r\u0131n, s\u0131k\u0131\u015ft\u0131rmay\u0131 varsay\u0131lan yapmadan \u00f6nce.<\/p>\n\n\n\n

      Ne zaman agresif s\u0131k\u0131\u015ft\u0131rma yap\u0131lmamal\u0131<\/h2>\n\n\n\n

      S\u0131k\u0131\u015ft\u0131rman\u0131n \u00f6d\u00fcnleri vard\u0131r. N\u00fcans\u0131 kald\u0131rabilir, belirsizli\u011fi gizleyebilir veya modelin ihtiya\u00e7 duydu\u011fu kan\u0131tlar\u0131 d\u00fczle\u015ftirebilir. Kesin ifadelerin \u00f6nemli oldu\u011fu durumlarda, modelin s\u00f6zle\u015fmeler veya politikalar \u00fczerinde mant\u0131k y\u00fcr\u00fctmesi gerekti\u011finde, al\u0131nt\u0131lar\u0131n korunmas\u0131 gerekti\u011finde veya kullan\u0131c\u0131 a\u00e7\u0131k\u00e7a kapsaml\u0131 kaynak materyali istedi\u011finde daha hafif s\u0131k\u0131\u015ft\u0131rma kullan\u0131n.<\/p>\n\n\n\n

      En g\u00fcvenli y\u00f6ntem kademeli s\u0131k\u0131\u015ft\u0131rmad\u0131r. Uygulaman\u0131zda y\u00fcksek do\u011fruluklu kaynak materyali bulundurun, modele kompakt ba\u011flam g\u00f6nderin ve g\u00f6rev do\u011frulama gerektirdi\u011finde orijinal kan\u0131t\u0131 tekrar al\u0131n.<\/p>\n\n\n\n

      SSS: LLM'ler i\u00e7in Token S\u0131k\u0131\u015ft\u0131rma<\/h2>\n\n\n\n

      LLM'ler i\u00e7in token s\u0131k\u0131\u015ft\u0131rma nedir?<\/h3>\n\n\n\n

      LLM'ler i\u00e7in token s\u0131k\u0131\u015ft\u0131rma, iyi bir yan\u0131t i\u00e7in gereken ger\u00e7ekleri, talimatlar\u0131 ve k\u0131s\u0131tlamalar\u0131 korurken model \u00e7a\u011fr\u0131s\u0131ndan \u00f6nce gereksiz giri\u015f metnini azaltmak anlam\u0131na gelir.<\/p>\n\n\n\n

      Token s\u0131k\u0131\u015ft\u0131rma daha k\u00fc\u00e7\u00fck bir model kullanmakla ayn\u0131 \u015fey mi?<\/h3>\n\n\n\n

      Hay\u0131r. S\u0131k\u0131\u015ft\u0131rma iste\u011fi azalt\u0131r. Model se\u00e7imi, bu iste\u011fin nereye gidece\u011fini belirler. En g\u00fc\u00e7l\u00fc yap\u0131 genellikle her ikisini yapar: \u00f6nce ba\u011flam\u0131 s\u0131k\u0131\u015ft\u0131r\u0131r, ard\u0131ndan do\u011fru modele y\u00f6nlendirir.<\/p>\n\n\n\n

      ShareAI istemleri otomatik olarak s\u0131k\u0131\u015ft\u0131r\u0131yor mu?<\/h3>\n\n\n\n

      S\u0131k\u0131\u015ft\u0131rma genellikle uygulama taraf\u0131nda bir tasar\u0131m tercihidir. ShareAI, uygulaman\u0131z iste\u011fi haz\u0131rlad\u0131ktan sonra model eri\u015fimi, y\u00f6nlendirme, yedekleme ve kullan\u0131m g\u00f6r\u00fcn\u00fcrl\u00fc\u011f\u00fc i\u00e7in AI pazaryeri ve API katman\u0131n\u0131 sa\u011flar.<\/p>\n\n\n\n

      S\u0131k\u0131\u015ft\u0131rma LLM maliyetlerini nas\u0131l azalt\u0131r?<\/h3>\n\n\n\n

      \u00c7o\u011fu AI API'si kullan\u0131m fiyatland\u0131rmas\u0131n\u0131 giri\u015f ve \u00e7\u0131k\u0131\u015f tokenlar\u0131 \u00fczerinden yapar. Giri\u015f tokenlar\u0131n\u0131 g\u00fcvenli bir \u015fekilde azalt\u0131rken kaliteyi sabit tutarsan\u0131z, ba\u015far\u0131l\u0131 g\u00f6rev ba\u015f\u0131na maliyet d\u00fc\u015febilir.<\/p>\n\n\n\n

      Token s\u0131k\u0131\u015ft\u0131rma yan\u0131t kalitesine zarar verebilir mi?<\/h3>\n\n\n\n

      Evet. A\u015f\u0131r\u0131 s\u0131k\u0131\u015ft\u0131rma kan\u0131tlar\u0131, n\u00fcanslar\u0131 veya k\u0131s\u0131tlamalar\u0131 kald\u0131rabilir. S\u0131k\u0131\u015ft\u0131r\u0131lm\u0131\u015f istemleri ger\u00e7ek g\u00f6revlerde test edin ve yan\u0131t kalitesini, yedekleme oran\u0131n\u0131 ve kullan\u0131c\u0131 d\u00fczeltmelerini izleyin.<\/p>\n\n\n\n

      Yap\u0131c\u0131lar s\u0131k\u0131\u015ft\u0131rma hakk\u0131nda ne bilmeli?<\/h3>\n\n\n\n

      Mevcut bir uygulamadan ShareAI \u00fczerinden AI kullan\u0131m\u0131n\u0131 y\u00f6nlendiren yap\u0131c\u0131lar, y\u00f6nlendirilen trafi\u011fi daha temiz tutmak i\u00e7in s\u0131k\u0131\u015ft\u0131rmay\u0131 kullanabilir. H\u00e2l\u00e2 ek \u00fccret veya marj belirleyebilir ve olu\u015fturulan kullan\u0131mdan ayl\u0131k \u00f6demeler alabilirler.<\/p>\n\n\n\n

      Token s\u0131k\u0131\u015ft\u0131rma RAG i\u00e7in faydal\u0131 m\u0131?<\/h3>\n\n\n\n

      Evet. RAG sistemleri genellikle gereksiz veya zay\u0131f \u015fekilde ilgili par\u00e7alar g\u00f6nderir. S\u0131k\u0131\u015ft\u0131rma, mevcut soruyu yan\u0131tlayan pasajlar\u0131 \u00e7o\u011faltabilir, filtreleyebilir ve \u00e7\u0131karabilir.<\/p>\n\n\n\n

      \u0130stem \u00f6nbellekleme s\u0131k\u0131\u015ft\u0131rman\u0131n yerine ge\u00e7ebilir mi?<\/h3>\n\n\n\n

      Hay\u0131r. \u0130stem \u00f6nbellekleme, desteklenen sistemlerde tekrarlanan \u00f6n eklerle yard\u0131mc\u0131 olabilir, ancak ba\u011flam g\u00fcr\u00fclt\u00fcl\u00fc, bayat, \u00e7o\u011falt\u0131lm\u0131\u015f veya g\u00f6rev i\u00e7in \u00e7ok b\u00fcy\u00fck oldu\u011funda s\u0131k\u0131\u015ft\u0131rma h\u00e2l\u00e2 \u00f6nemlidir.<\/p>\n\n\n\n

      Hangi ekipler token s\u0131k\u0131\u015ft\u0131rmadan en \u00e7ok fayda sa\u011flar?<\/h3>\n\n\n\n

      Uzun sohbet ge\u00e7mi\u015fi, ara\u00e7 a\u011f\u0131rl\u0131kl\u0131 ajanlar, belge i\u015f ak\u0131\u015flar\u0131, destek otomasyonu, ara\u015ft\u0131rma asistanlar\u0131 ve RAG sistemleri olan ekipler genellikle s\u0131k\u0131\u015ft\u0131rma ihtiyac\u0131n\u0131 en net \u015fekilde g\u00f6r\u00fcr.<\/p>\n\n\n\n

      S\u0131k\u0131\u015ft\u0131rma testine nas\u0131l ba\u015flamal\u0131y\u0131m?<\/h3>\n\n\n\n

      Pahal\u0131 bir i\u015f ak\u0131\u015f\u0131 se\u00e7in, temsilci istekleri yakalay\u0131n, s\u0131k\u0131\u015ft\u0131r\u0131lm\u0131\u015f versiyonlar olu\u015fturun ve token kullan\u0131m\u0131, cevap kalitesi, gecikme ve ba\u015far\u0131l\u0131 g\u00f6rev ba\u015f\u0131na maliyeti kar\u015f\u0131la\u015ft\u0131r\u0131n.<\/p>\n\n\n\n

      S\u0131k\u0131\u015ft\u0131rma yapay zeka y\u00f6nlendirme ile nas\u0131l \u00e7al\u0131\u015f\u0131r?<\/h3>\n\n\n\n

      S\u0131k\u0131\u015ft\u0131rma daha temiz bir istek haz\u0131rlar. Y\u00f6nlendirme, fiyat, gecikme, eri\u015filebilirlik, g\u00fcvenilirlik ve kalite ihtiya\u00e7lar\u0131na g\u00f6re o istek i\u00e7in en iyi modeli veya sa\u011flay\u0131c\u0131 rotas\u0131n\u0131 belirler.<\/p>\n\n\n\n

      9. Anahtar\u0131n\u0131z\u0131 olu\u015fturun, bir model se\u00e7in ve ba\u011flant\u0131y\u0131 do\u011frulay\u0131n. Buradan,<\/h2>\n\n\n\n

      Ba\u011flam \u015fi\u015fkinli\u011finin g\u00f6r\u00fcld\u00fc\u011f\u00fc bir i\u015f ak\u0131\u015f\u0131yla ba\u015flay\u0131n. G\u00fcr\u00fclt\u00fcl\u00fc k\u0131s\u0131mlar\u0131 s\u0131k\u0131\u015ft\u0131r\u0131n, \u00f6nemli kan\u0131tlar\u0131 saklay\u0131n, ard\u0131ndan ShareAI kullanarak bir API \u00fczerinden model rotalar\u0131n\u0131 kar\u015f\u0131la\u015ft\u0131r\u0131n. Pratik hedef basittir: daha az bo\u015fa harcanan token, daha az \u00f6nlenebilir y\u00fckseltme ve daha net kullan\u0131m verileri.<\/p>","protected":false},"excerpt":{"rendered":"

      Jeton s\u0131k\u0131\u015ft\u0131rma, ekiplerin model \u00e7a\u011fr\u0131lar\u0131ndan \u00f6nce g\u00fcr\u00fclt\u00fcl\u00fc ba\u011flam\u0131 kald\u0131rmas\u0131na yard\u0131mc\u0131 olur, maliyeti d\u00fc\u015f\u00fcr\u00fcr ve kalite \u00f6d\u00fcnlerini gizlemeden y\u00f6nlendirme kararlar\u0131n\u0131 iyile\u015ftirir.<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"cta-title":"Integrate one API","cta-description":"Access 150+ models with smart routing and failover.","cta-button-text":"View Docs","cta-button-link":"https:\/\/shareai.now\/documentation\/?utm_source=blog&utm_medium=content&utm_campaign=token-compression-for-llms","rank_math_title":"Token Compression for LLMs: Cut Context Cost Before Routing","rank_math_description":"Token compression for LLMs reduces context bloat before routing. Learn where to compress prompts, tool outputs, RAG chunks, and logs.","rank_math_focus_keyword":"token compression for LLMs, LLM token compression, reduce LLM token costs, AI API cost optimization","footnotes":""},"categories":[6,4],"tags":[42,46,224,225,223],"class_list":["post-3114","post","type-post","status-publish","format-standard","hentry","category-insights","category-developers","tag-ai-api-routing","tag-ai-gateway","tag-llm-cost-optimization","tag-rag","tag-token-compression"],"_links":{"self":[{"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/posts\/3114","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/comments?post=3114"}],"version-history":[{"count":2,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/posts\/3114\/revisions"}],"predecessor-version":[{"id":3123,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/posts\/3114\/revisions\/3123"}],"wp:attachment":[{"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/media?parent=3114"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/categories?post=3114"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/tags?post=3114"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}