{"id":27685,"date":"2025-08-17T14:32:35","date_gmt":"2025-08-17T11:32:35","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/llm-hassasiyetini-arttirmak-pydantic-ve-instructor-ile-yapilandirilmis-cikti-ustalasma\/"},"modified":"2025-08-17T14:32:35","modified_gmt":"2025-08-17T11:32:35","slug":"llm-hassasiyetini-arttirmak-pydantic-ve-instructor-ile-yapilandirilmis-cikti-ustalasma","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/llm-hassasiyetini-arttirmak-pydantic-ve-instructor-ile-yapilandirilmis-cikti-ustalasma\/","title":{"rendered":"LLM Hassasiyetini Artt\u0131rmak: Pydantic ve Instructor ile Yap\u0131land\u0131r\u0131lm\u0131\u015f \u00c7\u0131kt\u0131 Ustala\u015fma"},"content":{"rendered":"

LLM Hassasiyetini Artt\u0131rmak: Pydantic ve Instructor ile Yap\u0131land\u0131r\u0131lm\u0131\u015f \u00c7\u0131kt\u0131 Ustala\u015fma<\/p>\n

B\u00fcy\u00fck dil modelleri (LLM’ler) inan\u0131lmaz yeteneklere sahip olsa da, tutarl\u0131 ve yap\u0131land\u0131r\u0131lm\u0131\u015f \u00e7\u0131kt\u0131lar \u00fcretmek konusunda zorluk ya\u015fayabilirler. Bu durum, \u00f6zellikle veri analizi, raporlama veya API geli\u015ftirme gibi uygulamalarda verimlili\u011fi ciddi oranda etkiler. Bu makalede, Pydantic ve Instructor k\u00fct\u00fcphanelerini kullanarak LLM’lerden elde edilen \u00e7\u0131kt\u0131y\u0131 nas\u0131l yap\u0131land\u0131raca\u011f\u0131n\u0131z\u0131, hatalar\u0131 nas\u0131l azaltaca\u011f\u0131n\u0131z\u0131 ve hassasiyeti nas\u0131l art\u0131raca\u011f\u0131n\u0131z\u0131 ad\u0131m ad\u0131m \u00f6\u011freneceksiniz. Pydantic’in veri do\u011frulama g\u00fcc\u00fc ve Instructor’\u0131n LLM’leri e\u011fitme yetene\u011fi sayesinde, LLM’lerinizden tam olarak bekledi\u011finiz gibi \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flayacaks\u0131n\u0131z.<\/p>\n

Pydantic ve Instructor: Temel Kavramlar<\/h2>\n

\u00d6ncelikle, Pydantic ve Instructor k\u00fct\u00fcphanelerinin ne oldu\u011funu ve neden LLM hassasiyetini art\u0131rmak i\u00e7in ideal bir kombinasyon olu\u015fturduklar\u0131n\u0131 anlamak \u00f6nemlidir. Pydantic, Python’da veri do\u011frulama ve yap\u0131land\u0131rma i\u00e7in kullan\u0131lan g\u00fc\u00e7l\u00fc bir k\u00fct\u00fcphanedir. Belirledi\u011finiz bir \u015fema (schema) kullanarak, gelen verilerin do\u011fru formatta ve t\u00fcrde olup olmad\u0131\u011f\u0131n\u0131 kontrol eder ve olas\u0131 hatalar\u0131 yakalar. Bu, LLM’lerden gelen verilerin do\u011fru \u015fekilde i\u015flenmesini ve beklenmedik hatalardan ka\u00e7\u0131n\u0131lmas\u0131n\u0131 sa\u011flar. \u00d6te yandan, Instructor, LLM’leri \u00f6zel g\u00f6revler i\u00e7in e\u011fitmek \u00fczere tasarlanm\u0131\u015f, kullan\u0131m\u0131 kolay bir k\u00fct\u00fcphanedir. \u00d6rne\u011fin, LLM’lerinize belirli bir formatta \u00e7\u0131kt\u0131 \u00fcretmeyi \u00f6\u011freterek, \u00e7\u0131kt\u0131n\u0131n tutarl\u0131l\u0131\u011f\u0131n\u0131 ve do\u011frulu\u011funu \u00f6nemli \u00f6l\u00e7\u00fcde art\u0131rabilirsiniz.<\/p>\n

Pydantic, veri do\u011frulama ve yap\u0131land\u0131rma i\u00e7in Python’da yayg\u0131n olarak kullan\u0131lan bir ara\u00e7t\u0131r. Veri yap\u0131lar\u0131n\u0131z\u0131 Python s\u0131n\u0131flar\u0131 olarak tan\u0131mlar ve Pydantic, bu s\u0131n\u0131flar\u0131n \u00f6rneklerinin ge\u00e7erli olup olmad\u0131\u011f\u0131n\u0131 otomatik olarak do\u011frular. Bu, tip kontrol\u00fc, aral\u0131k do\u011frulamas\u0131, zorunlu alanlar ve daha fazlas\u0131n\u0131 i\u00e7erir. Instructor ise, LLM’leri daha hassas ve tutarl\u0131 hale getirmek i\u00e7in \u00f6rnekler \u00fczerinde e\u011fitimini kolayla\u015ft\u0131r\u0131r. Bu sayede, LLM’lerinizin, belirli bir formatta \u00e7\u0131kt\u0131 \u00fcretme olas\u0131l\u0131\u011f\u0131n\u0131 art\u0131rabilirsiniz. \u0130ki k\u00fct\u00fcphane birle\u015fti\u011finde, LLM’lerinizden hem do\u011fru hem de yap\u0131land\u0131r\u0131lm\u0131\u015f \u00e7\u0131kt\u0131 elde etmenizi sa\u011flar.<\/p>\n

Pydantic ile Veri Do\u011frulama: Ad\u0131m Ad\u0131m Uygulama<\/h2>\n

\u015eimdi, Pydantic’i kullanarak LLM \u00e7\u0131kt\u0131lar\u0131n\u0131 nas\u0131l do\u011frulayabilece\u011finizi ad\u0131m ad\u0131m g\u00f6relim. \u00d6ncelikle, Pydantic’i kurman\u0131z gerekir:<\/p>\n

pip install pydantic<\/pre>\n
<\/code><\/p>\n
Daha sonra, LLM'den gelen verileri temsil eden bir Pydantic modeli tan\u0131mlayal\u0131m.  \u00d6rne\u011fin, bir \u00fcr\u00fcn\u00fcn ad\u0131n\u0131, fiyat\u0131n\u0131 ve a\u00e7\u0131klamas\u0131n\u0131 i\u00e7eren bir model olu\u015ftural\u0131m:<\/p>\n
from pydantic import BaseModel, Field\n\nclass Product(BaseModel):\n    name: str = Field(..., min_length=3, max_length=50)\n    price: float = Field(..., gt=0)\n    description: str | None = None<\/pre>\n
<\/code><\/p>\n
Bu modelde, name<\/code> alan\u0131 en az 3 ve en fazla 50 karakter uzunlu\u011funda olmal\u0131d\u0131r. price<\/code> alan\u0131 s\u0131f\u0131rdan b\u00fcy\u00fck olmal\u0131d\u0131r ve description<\/code> alan\u0131 iste\u011fe ba\u011fl\u0131d\u0131r.  LLM'den gelen verileri bu modelle do\u011frulayarak hatalar\u0131 \u00f6nleyebilirsiniz. \u00d6rne\u011fin:<\/p>\n
from pydantic import ValidationError\n\nllm_output = {\"name\": \"Kitap\", \"price\": 10.99, \"description\": \"\u00c7ok g\u00fczel bir kitap\"}\n\ntry:\n    product = Product(**llm_output)\n    print(product)\nexcept ValidationError as e:\n    print(e)\n\nllm_output_error = {\"name\": \"K\", \"price\": -5, \"description\": \"\u00c7ok g\u00fczel bir kitap\"}\n\ntry:\n    product = Product(**llm_output_error)\n    print(product)\nexcept ValidationError as e:\n    print(e)<\/pre>\n
<\/code><\/p>\n
Bu kod blo\u011fu, LLM'den gelen verilerin Pydantic modeli ile uyumlulu\u011funu kontrol eder ve hatal\u0131 veriler i\u00e7in bir ValidationError<\/code> istisnas\u0131 olu\u015fturur.  Bu, hatalar\u0131 erken a\u015famada yakalaman\u0131za ve uygulaman\u0131z\u0131n \u00e7\u00f6kmesini \u00f6nlemenize yard\u0131mc\u0131 olur.<\/p>\n
Instructor ile LLM E\u011fitimi: Pratik \u00d6rnekler<\/h2>\n
Instructor, LLM'leri daha hassas ve tutarl\u0131 bir \u015fekilde yap\u0131land\u0131r\u0131lm\u0131\u015f \u00e7\u0131kt\u0131lar \u00fcretmeleri i\u00e7in e\u011fitmenize olanak tan\u0131r.  \u00d6ncelikle, Instructor'\u0131 kurman\u0131z gerekir:<\/p>\n
pip install instructor<\/pre>\n
<\/code><\/p>\n
\u015eimdi,  bir \u00fcr\u00fcn\u00fcn \u00f6zelliklerini i\u00e7eren bir metin vererek, LLM'nin Pydantic modelimizle uyumlu bir JSON \u00e7\u0131kt\u0131s\u0131 \u00fcretmesini sa\u011flayal\u0131m.  \u00d6rnek bir e\u011fitim verisi \u015f\u00f6yle olabilir:<\/p>\n
training_data = [\n    (\"Ad\u0131: Laptop, Fiyat\u0131: 1500 TL, A\u00e7\u0131klama: G\u00fc\u00e7l\u00fc bir laptop\", '{\"name\": \"Laptop\", \"price\": 1500.0, \"description\": \"G\u00fc\u00e7l\u00fc bir laptop\"}'),\n    (\"Ad\u0131: Telefon, Fiyat\u0131: 3000 TL, A\u00e7\u0131klama: Yeni nesil ak\u0131ll\u0131 telefon\", '{\"name\": \"Telefon\", \"price\": 3000.0, \"description\": \"Yeni nesil ak\u0131ll\u0131 telefon\"}'),\n]<\/pre>\n
<\/code><\/p>\n
Daha sonra, bu verileri kullanarak bir Instructor modeli e\u011fitebilir ve LLM'den  Pydantic modelini kullanarak  verileri do\u011fru bir \u015fekilde yap\u0131land\u0131rmas\u0131n\u0131 isteyebilirsiniz.  Bu,  LLM'nin \u00e7\u0131kt\u0131 format\u0131n\u0131 kontrol etmenizi ve tutars\u0131z veya hatal\u0131 verileri azaltman\u0131z\u0131 sa\u011flar.<\/p>\n
Ger\u00e7ek D\u00fcnya Senaryolar\u0131: Vaka Analizleri<\/h2>\n
Pydantic ve Instructor'\u00fcn ger\u00e7ek d\u00fcnya uygulamalar\u0131na birka\u00e7 \u00f6rnek verelim.  \u00d6rne\u011fin, bir e-ticaret sitesinde \u00fcr\u00fcn bilgilerini otomatik olarak \u00e7\u0131karmak i\u00e7in LLM kullanabilirsiniz.  LLM, \u00fcr\u00fcn a\u00e7\u0131klamalar\u0131ndan ad\u0131, fiyat\u0131n\u0131 ve a\u00e7\u0131klamas\u0131n\u0131 \u00e7\u0131kar\u0131r, ancak bu bilgiler tutars\u0131z veya hatal\u0131 olabilir.  Pydantic, bu verilerin do\u011fru formatta ve t\u00fcrde olup olmad\u0131\u011f\u0131n\u0131 kontrol ederken, Instructor ise LLM'yi daha tutarl\u0131 ve yap\u0131land\u0131r\u0131lm\u0131\u015f \u00e7\u0131kt\u0131lar \u00fcretmesi i\u00e7in e\u011fitebilir.  Benzer \u015fekilde, bir m\u00fc\u015fteri hizmetleri robotu i\u00e7in LLM kullan\u0131yorsan\u0131z, Pydantic, m\u00fc\u015fteri sorular\u0131na verilen cevaplar\u0131n do\u011fru formatta ve t\u00fcrde olup olmad\u0131\u011f\u0131n\u0131 do\u011frulayabilir ve Instructor, LLM'yi daha do\u011fal ve anla\u015f\u0131l\u0131r cevaplar \u00fcretmesi i\u00e7in e\u011fitebilir.<\/p>\n
Bir di\u011fer \u00f6rnek ise, bir haber ajans\u0131n\u0131n haber metinlerinden belirli bilgilerin (tarih, yer, olay) \u00e7\u0131kar\u0131lmas\u0131d\u0131r.  LLM bu bilgileri \u00e7\u0131karabilir ancak farkl\u0131 formatlarda ve yanl\u0131\u015f olabilir. Pydantic bu verileri standart formata sokar ve Instructor ise LLM'i bu format\u0131 tutarl\u0131 bir \u015fekilde kullanmas\u0131 i\u00e7in e\u011fitir.  Bu sayede, haber verileri daha d\u00fczenli bir \u015fekilde saklanabilir ve i\u015flenebilir.<\/p>\n
\u0130leri D\u00fczey Teknikler: Performans Optimizasyonu ve G\u00fcvenlik<\/h2>\n
LLM'lerin hassasiyetini art\u0131rmak i\u00e7in Pydantic ve Instructor'\u00fc kullanman\u0131n daha ileri d\u00fczey teknikleri vard\u0131r.  \u00d6rne\u011fin, Pydantic'in geli\u015fmi\u015f do\u011frulama \u00f6zellikleri kullan\u0131larak daha karma\u015f\u0131k veri yap\u0131lar\u0131n\u0131 do\u011frulayabilirsiniz.  Ayr\u0131ca, Instructor'\u00fc kullanarak LLM'leri belirli bir do\u011fruluk d\u00fczeyinde \u00e7\u0131kt\u0131 \u00fcretmeleri i\u00e7in e\u011fitebilirsiniz.  Performans\u0131 iyile\u015ftirmek i\u00e7in, e\u011fitim verilerinizi dikkatlice se\u00e7meli ve LLM'nin performans\u0131n\u0131 d\u00fczenli olarak izlemelisiniz.  Daha b\u00fcy\u00fck veri setleriyle ve daha geli\u015fmi\u015f LLM modelleriyle daha iyi sonu\u00e7lar alabilirsiniz.  Ancak,  her zaman LLM \u00e7\u0131kt\u0131lar\u0131n\u0131 dikkatlice g\u00f6zden ge\u00e7irmeli ve olas\u0131 hatalar\u0131 kontrol etmelisiniz.<\/p>\n
G\u00fcvenlik a\u00e7\u0131s\u0131ndan, LLM'lerden gelen verileri her zaman g\u00fcvenilir bir kaynak olarak de\u011ferlendirmemelisiniz.  Pydantic, k\u00f6t\u00fc ama\u00e7l\u0131 kod veya hatal\u0131 verileri yakalamaya yard\u0131mc\u0131 olabilir, ancak tamamen g\u00fcvenli olmad\u0131\u011f\u0131n\u0131 unutmay\u0131n.  LLM'lerin g\u00fcvenlik a\u00e7\u0131klar\u0131n\u0131 \u00f6nlemek i\u00e7in,  girdi do\u011frulama ve \u00e7\u0131kt\u0131 filtreleme gibi g\u00fcvenlik \u00f6nlemlerini uygulaman\u0131z gerekir.  \u00d6rne\u011fin, kullan\u0131c\u0131 taraf\u0131ndan sa\u011flanan verileri do\u011frudan LLM'ye g\u00f6ndermeden \u00f6nce Pydantic ile do\u011frulamak \u00f6nemlidir.<\/p>\n
\n
Dikkat!<\/h3>\n
LLM'ler her zaman do\u011fru veya g\u00fcvenilir sonu\u00e7lar \u00fcretmeyebilir.  Pydantic ve Instructor, \u00e7\u0131kt\u0131n\u0131n do\u011frulu\u011funu ve tutarl\u0131l\u0131\u011f\u0131n\u0131 art\u0131rmaya yard\u0131mc\u0131 olur, ancak hatalar\u0131 tamamen ortadan kald\u0131rmaz.  Her zaman \u00e7\u0131kt\u0131y\u0131 g\u00f6zden ge\u00e7irmeli ve olas\u0131 hatalar\u0131 kontrol etmelisiniz.<\/p>\n<\/div>\n
\u00d6\u011frenme Yol Haritas\u0131<\/h2>\n
Yeni Ba\u015flayanlar \u0130\u00e7in<\/h3>\n
Pydantic ve Instructor'\u00fcn temellerini \u00f6\u011frenin. Basit veri do\u011frulama modelleri olu\u015fturun ve k\u00fc\u00e7\u00fck e\u011fitim veri setleri ile LLM'leri e\u011fitin.  Kendi basit projelerinizde bunlar\u0131 uygulay\u0131n.<\/p>\n
Orta Seviye<\/h3>\n
Daha karma\u015f\u0131k Pydantic modellerini ve geli\u015fmi\u015f do\u011frulama kurallar\u0131n\u0131 ke\u015ffedin.  Daha b\u00fcy\u00fck e\u011fitim veri setleri ile deney yap\u0131n ve LLM performans\u0131n\u0131 nas\u0131l optimize edece\u011finizi \u00f6\u011frenin.  Ger\u00e7ek d\u00fcnya senaryolar\u0131na odaklan\u0131n ve kendi \u00e7\u00f6z\u00fcmlerinizi geli\u015ftirin.<\/p>\n
\u0130leri Seviye<\/h3>\n
Pydantic ve Instructor'\u00fcn geli\u015fmi\u015f \u00f6zelliklerini kullan\u0131n.  Performans optimizasyonu ve g\u00fcvenlik \u00f6nlemleri konusunda uzmanla\u015f\u0131n.  Kendi LLM uygulamalar\u0131n\u0131z\u0131 geli\u015ftirin ve a\u00e7\u0131k kaynakl\u0131 projelere katk\u0131da bulunun.<\/p>\n
Sonu\u00e7<\/h2>\n
Pydantic ve Instructor, LLM'lerin hassasiyetini ve tutarl\u0131l\u0131\u011f\u0131n\u0131 \u00f6nemli \u00f6l\u00e7\u00fcde art\u0131rmak i\u00e7in g\u00fc\u00e7l\u00fc ara\u00e7lard\u0131r.  Bu makalede, bu iki k\u00fct\u00fcphanenin temel kavramlar\u0131n\u0131 ve pratik uygulamalar\u0131n\u0131 inceledik.  Ger\u00e7ek d\u00fcnya senaryolar\u0131na ve ileri d\u00fczey tekniklere de\u011finerek,  LLM'lerinizin potansiyelini en \u00fcst d\u00fczeye \u00e7\u0131karman\u0131z i\u00e7in gereken bilgi ve becerileri kazand\u0131\u011f\u0131n\u0131zdan emin olduk.<\/p>\n
S\u0131k\u00e7a Sorulan Sorular (SSS)<\/h3>\n
    \n
  • Pydantic ve Instructor'\u00fc hangi LLM'lerle kullanabilirim?<\/strong>  Pydantic,  LLM'den gelen verileri do\u011frulamak i\u00e7in kullan\u0131labilen bir Python k\u00fct\u00fcphanesidir. Instructor ise \u00e7e\u015fitli LLM'ler ile \u00e7al\u0131\u015fabilir.  OpenAI'nin GPT modelleri, Hugging Face modelleri ve di\u011fer LLM'ler ile uyumludur.<\/li>\n
  • Pydantic modelleri ne kadar karma\u015f\u0131k olabilir?<\/strong>  Pydantic modelleri, istedi\u011finiz kadar karma\u015f\u0131k olabilir.  Karma\u015f\u0131k veri yap\u0131lar\u0131n\u0131 ve do\u011frulama kurallar\u0131n\u0131 destekler.<\/li>\n
  • Instructor ile ne kadar b\u00fcy\u00fck veri setleri kullanabilirim?<\/strong>  Kullanabilece\u011finiz veri seti boyutu, kaynaklar\u0131n\u0131za ba\u011fl\u0131d\u0131r.  Daha b\u00fcy\u00fck veri setleri daha iyi sonu\u00e7lar verebilir, ancak daha fazla hesaplama g\u00fcc\u00fc gerektirir.<\/li>\n
  • Pydantic ve Instructor'\u00fcn performans\u0131n\u0131 nas\u0131l \u00f6l\u00e7ebilirim?<\/strong>  LLM'nin do\u011frulu\u011funu ve tutarl\u0131l\u0131\u011f\u0131n\u0131 \u00f6l\u00e7mek i\u00e7in, test veri setlerindeki performans\u0131n\u0131 de\u011ferlendirebilirsiniz. Daha fazla bilgi i\u00e7in (https:\/\/fatihsoysal.com) inceleyebilirsiniz.<\/li>\n
  • Pydantic ve Instructor kullanman\u0131n g\u00fcvenlik riskleri nelerdir?<\/strong> LLM'ler her zaman g\u00fcvenilir sonu\u00e7lar \u00fcretmeyebilir. K\u00f6t\u00fc ama\u00e7l\u0131 kod veya hatal\u0131 verileri yakalamak i\u00e7in girdi do\u011frulama ve \u00e7\u0131kt\u0131 filtreleme gibi g\u00fcvenlik \u00f6nlemleri uygulanmal\u0131d\u0131r.<\/li>\n<\/ul>\n
    Yazar: Fatih Soysal<\/p>\n","protected":false},"excerpt":{"rendered":"LLM Hassasiyetini Artt\u0131rmak: Pydantic ve Instructor ile Yap\u0131land\u0131r\u0131lm\u0131\u015f \u00c7\u0131kt\u0131 Ustala\u015fma B\u00fcy\u00fck dil modelleri (LLM’ler) inan\u0131lmaz yeteneklere sahip olsa…","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-27685","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\nLLM Hassasiyetini Artt\u0131rmak: Pydantic ve Instructor ile Yap\u0131land\u0131r\u0131lm\u0131\u015f \u00c7\u0131kt\u0131 Ustala\u015fma<\/title>\n<meta name=\"description\" content=\"B\u00fcy\u00fck dil modelleri (LLM'ler) inan\u0131lmaz yeteneklere sahip olsa da, tutarl\u0131 ve yap\u0131land\u0131r\u0131lm\u0131\u015f \u00e7\u0131kt\u0131lar \u00fcretmek konusunda zorluk ya\u015fayabilirler. Bu durum, \u00f6zellikle veri analizi, raporlama veya API geli\u015ftirme gibi uygulamalarda verimlili\u011fi ciddi oranda etkiler. Bu makalede, Pydantic ve Instructor k\u00fct\u00fcphanelerini kullanarak LLM'lerden elde edilen \u00e7\u0131kt\u0131y\u0131 nas\u0131l yap\u0131land\u0131raca\u011f\u0131n\u0131z\u0131, hatalar\u0131 nas\u0131l azaltaca\u011f\u0131n\u0131z\u0131 ve hassasiyeti nas\u0131l art\u0131raca\u011f\u0131n\u0131z\u0131 ad\u0131m ad\u0131m \u00f6\u011freneceksiniz. Pydantic'in veri do\u011frulama g\u00fcc\u00fc ve Instructor'\u0131n LLM'leri e\u011fitme yetene\u011fi sayesinde, LLM'lerinizden tam olarak bekledi\u011finiz gibi \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flayacaks\u0131n\u0131z.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fatihsoysal.com\/blog\/llm-hassasiyetini-arttirmak-pydantic-ve-instructor-ile-yapilandirilmis-cikti-ustalasma\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"LLM Hassasiyetini Artt\u0131rmak: Pydantic ve Instructor ile Yap\u0131land\u0131r\u0131lm\u0131\u015f \u00c7\u0131kt\u0131 Ustala\u015fma\" \/>\n<meta property=\"og:description\" content=\"B\u00fcy\u00fck dil modelleri (LLM'ler) inan\u0131lmaz yeteneklere sahip olsa da, tutarl\u0131 ve yap\u0131land\u0131r\u0131lm\u0131\u015f \u00e7\u0131kt\u0131lar \u00fcretmek konusunda zorluk ya\u015fayabilirler. Bu durum, \u00f6zellikle veri analizi, raporlama veya API geli\u015ftirme gibi uygulamalarda verimlili\u011fi ciddi oranda etkiler. Bu makalede, Pydantic ve Instructor k\u00fct\u00fcphanelerini kullanarak LLM'lerden elde edilen \u00e7\u0131kt\u0131y\u0131 nas\u0131l yap\u0131land\u0131raca\u011f\u0131n\u0131z\u0131, hatalar\u0131 nas\u0131l azaltaca\u011f\u0131n\u0131z\u0131 ve hassasiyeti nas\u0131l art\u0131raca\u011f\u0131n\u0131z\u0131 ad\u0131m ad\u0131m \u00f6\u011freneceksiniz. Pydantic'in veri do\u011frulama g\u00fcc\u00fc ve Instructor'\u0131n LLM'leri e\u011fitme yetene\u011fi sayesinde, LLM'lerinizden tam olarak bekledi\u011finiz gibi \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flayacaks\u0131n\u0131z.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fatihsoysal.com\/blog\/llm-hassasiyetini-arttirmak-pydantic-ve-instructor-ile-yapilandirilmis-cikti-ustalasma\/\" \/>\n<meta property=\"og:site_name\" content=\"Kodlar\u0131n Gizemli D\u00fcnyas\u0131\" \/>\n<meta property=\"article:published_time\" content=\"2025-08-17T11:32:35+00:00\" \/>\n<meta name=\"author\" content=\"Fatih Soysal\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Yazan:\" \/>\n\t<meta name=\"twitter:data1\" content=\"Fatih Soysal\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tahmini okuma s\u00fcresi\" \/>\n\t<meta name=\"twitter:data2\" content=\"8 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/llm-hassasiyetini-arttirmak-pydantic-ve-instructor-ile-yapilandirilmis-cikti-ustalasma\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/llm-hassasiyetini-arttirmak-pydantic-ve-instructor-ile-yapilandirilmis-cikti-ustalasma\/\"},\"author\":{\"name\":\"Fatih Soysal\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"headline\":\"LLM Hassasiyetini Artt\u0131rmak: Pydantic ve Instructor ile Yap\u0131land\u0131r\u0131lm\u0131\u015f \u00c7\u0131kt\u0131 Ustala\u015fma\",\"datePublished\":\"2025-08-17T11:32:35+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/llm-hassasiyetini-arttirmak-pydantic-ve-instructor-ile-yapilandirilmis-cikti-ustalasma\/\"},\"wordCount\":1483,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/llm-hassasiyetini-arttirmak-pydantic-ve-instructor-ile-yapilandirilmis-cikti-ustalasma\/#respond\"]}],\"copyrightYear\":\"2025\",\"copyrightHolder\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/llm-hassasiyetini-arttirmak-pydantic-ve-instructor-ile-yapilandirilmis-cikti-ustalasma\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/llm-hassasiyetini-arttirmak-pydantic-ve-instructor-ile-yapilandirilmis-cikti-ustalasma\/\",\"name\":\"LLM Hassasiyetini Artt\u0131rmak: Pydantic ve Instructor ile Yap\u0131land\u0131r\u0131lm\u0131\u015f \u00c7\u0131kt\u0131 Ustala\u015fma\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\"},\"datePublished\":\"2025-08-17T11:32:35+00:00\",\"description\":\"B\u00fcy\u00fck dil modelleri (LLM'ler) inan\u0131lmaz yeteneklere sahip olsa da, tutarl\u0131 ve yap\u0131land\u0131r\u0131lm\u0131\u015f \u00e7\u0131kt\u0131lar \u00fcretmek konusunda zorluk ya\u015fayabilirler. 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