B\u00fcy\u00fck Dil Modelleri (LLM’ler), karma\u015f\u0131k g\u00f6revleri anlama ve yerine getirme yetenekleriyle yapay zeka d\u00fcnyas\u0131nda devrim yarat\u0131yor. Ancak bu yeteneklerin ger\u00e7ek d\u00fcnya uygulamalar\u0131na d\u00f6n\u00fc\u015fmesi, LLM’lerin d\u0131\u015f ara\u00e7larla nas\u0131l etkile\u015fime girdi\u011fi sorusunu g\u00fcndeme getiriyor. Bu makalede, LLM’lerin ara\u00e7lar\u0131 kullanma stratejileri olan ReAct (Reasoning and Acting) ve Tool Calling (Fonksiyon \u00c7a\u011f\u0131rma) yakla\u015f\u0131mlar\u0131n\u0131 derinlemesine inceleyece\u011fiz. Amac\u0131m\u0131z, LLM’lerin ne zaman ve nas\u0131l karar vermesi gerekti\u011fini, ancak kritik eylemleri asla do\u011frudan y\u00fcr\u00fctmemesi gerekti\u011fini vurgulamakt\u0131r.<\/p>\n
ReAct, “Reasoning” (Ak\u0131l Y\u00fcr\u00fctme) ve “Acting” (Eyleme Ge\u00e7me) kelimelerinin birle\u015fimiyle olu\u015fan, LLM’lerin karma\u015f\u0131k g\u00f6revleri yerine getirirken hem ak\u0131l y\u00fcr\u00fctme hem de d\u0131\u015f ara\u00e7lar\u0131 kullanma yetene\u011fini birle\u015ftiren bir paradigm\u0131d\u0131r. Bu yakla\u015f\u0131m, LLM’in bir g\u00f6revi ad\u0131m ad\u0131m planlamas\u0131n\u0131, her ad\u0131mda d\u00fc\u015f\u00fcnmesini ve gerekti\u011finde d\u0131\u015f ara\u00e7lar\u0131 kullanarak bilgi edinmesini veya eylem ger\u00e7ekle\u015ftirmesini sa\u011flar.<\/p>\n
ReAct’in \u00f6z\u00fcnde, LLM’in bir d\u00f6ng\u00fc i\u00e7inde \u00e7al\u0131\u015fmas\u0131 yatar: G\u00f6zlemle, Ak\u0131l Y\u00fcr\u00fct, Eyleme Ge\u00e7. LLM, mevcut duruma (g\u00f6zlem) g\u00f6re bir sonraki ad\u0131m\u0131 (ak\u0131l y\u00fcr\u00fctme) belirler ve bu ad\u0131ma uygun bir eylemi (eyleme ge\u00e7me) tetikler. Bu eylem genellikle bir arac\u0131 \u00e7a\u011f\u0131rmak veya bir \u00e7\u0131kt\u0131 \u00fcretmek olabilir. Eylemin sonucu tekrar LLM’e g\u00f6zlem olarak sunulur ve d\u00f6ng\u00fc devam eder.<\/p>\n
Bir ReAct i\u015f ak\u0131\u015f\u0131 genellikle \u015fu ad\u0131mlar\u0131 i\u00e7erir:<\/p>\n
\u00d6rnek bir ReAct prompt yap\u0131s\u0131:<\/p>\n
\nHuman: Bug\u00fcn \u0130stanbul'da hava nas\u0131l ve dolar kuru ka\u00e7 TL?\nThought: Hava durumunu \u00f6\u011frenmek i\u00e7in bir hava durumu arac\u0131 ve dolar kurunu \u00f6\u011frenmek i\u00e7in bir finans arac\u0131 kullanmal\u0131y\u0131m. \u00d6nce hava durumunu sorgulayay\u0131m.\nAction: call_tool(\"get_weather\", {\"city\": \"Istanbul\"})\nObservation: \u0130stanbul'da hava durumu: Par\u00e7al\u0131 bulutlu, 25\u00b0C.\nThought: \u015eimdi dolar kurunu \u00f6\u011frenmeliyim.\nAction: call_tool(\"get_exchange_rate\", {\"currency\": \"USD\"})\nObservation: 1 USD = 32.50 TL.\nThought: Her iki bilgiyi de edindim. Art\u0131k kullan\u0131c\u0131ya yan\u0131t verebilirim.\nAction: finish(\"Bug\u00fcn \u0130stanbul'da hava par\u00e7al\u0131 bulutlu ve 25\u00b0C. Dolar kuru ise 32.50 TL.\")\n<\/pre>\n<\/code><\/p>\n
ReAct'in Avantajlar\u0131 ve Dezavantajlar\u0131<\/h4>\n\n- Avantajlar\u0131:<\/strong> Karma\u015f\u0131k, \u00e7ok ad\u0131ml\u0131 g\u00f6revler i\u00e7in y\u00fcksek esneklik sa\u011flar. LLM'in d\u00fc\u015f\u00fcnce s\u00fcrecini takip etmek daha kolayd\u0131r, bu da hata ay\u0131klama ve \u015feffafl\u0131k a\u00e7\u0131s\u0131ndan faydal\u0131d\u0131r. Bilgi edinme ve eylem ger\u00e7ekle\u015ftirme ad\u0131mlar\u0131n\u0131 dinamik olarak y\u00f6netebilir.<\/li>\n
- Dezavantajlar\u0131:<\/strong> Her ad\u0131mda ak\u0131l y\u00fcr\u00fctme gerektirdi\u011finden daha fazla token t\u00fcketimine ve dolay\u0131s\u0131yla daha y\u00fcksek maliyete yol a\u00e7abilir. Prompt m\u00fchendisli\u011fi daha karma\u015f\u0131k olabilir. Gecikme s\u00fcresi (latency) daha y\u00fcksek olabilir.<\/li>\n<\/ul>\n
Tool Calling (Fonksiyon \u00c7a\u011f\u0131rma) Nedir?<\/h3>\n
Tool Calling (veya Function Calling), LLM'lerin \u00f6nceden tan\u0131mlanm\u0131\u015f fonksiyonlar\u0131 veya ara\u00e7lar\u0131 \u00e7a\u011f\u0131rmas\u0131na olanak tan\u0131yan daha do\u011frudan bir yakla\u015f\u0131md\u0131r. Bu modelde, LLM do\u011frudan bir eylemi tetiklemek yerine, hangi arac\u0131 hangi parametrelerle \u00e7a\u011f\u0131rmas\u0131 gerekti\u011fine dair bir yap\u0131land\u0131r\u0131lm\u0131\u015f \u00e7\u0131kt\u0131 (genellikle JSON format\u0131nda) \u00fcretir. Ger\u00e7ek ara\u00e7 \u00e7a\u011f\u0131rma i\u015flemi, LLM'in d\u0131\u015f\u0131nda, bir orkestrasyon katman\u0131 taraf\u0131ndan ger\u00e7ekle\u015ftirilir.<\/p>\n
Tool Calling'in Temel Prensibi<\/h4>\n
Tool Calling, LLM'in temel g\u00f6revinin, kullan\u0131c\u0131n\u0131n iste\u011fini anlayarak mevcut ara\u00e7 setinden en uygun olan\u0131 se\u00e7mek ve bu arac\u0131 do\u011fru parametrelerle \u00e7a\u011f\u0131rmak i\u00e7in gerekli bilgiyi \u00fcretmek oldu\u011fu prensibine dayan\u0131r. LLM, bir \"niyet\" tespit edici ve parametre \u00e7\u0131kar\u0131c\u0131 gibi davran\u0131r.<\/p>\n
Tool Calling \u0130\u015f Ak\u0131\u015f\u0131<\/h4>\n
Bir Tool Calling i\u015f ak\u0131\u015f\u0131 genellikle \u015fu ad\u0131mlar\u0131 izler:<\/p>\n
\n- Kullan\u0131c\u0131 \u0130ste\u011fi:<\/strong> Kullan\u0131c\u0131 bir istekte bulunur.<\/li>\n
- LLM De\u011ferlendirmesi:<\/strong> LLM, kullan\u0131c\u0131n\u0131n iste\u011fini ve kendisine sunulan ara\u00e7lar\u0131n tan\u0131mlar\u0131n\u0131 (isimleri, a\u00e7\u0131klamalar\u0131, beklenen parametreleri) de\u011ferlendirir.<\/li>\n
- Ara\u00e7 \u00c7a\u011f\u0131rma \u00d6nerisi:<\/strong> LLM, e\u011fer uygun bir ara\u00e7 varsa, bu arac\u0131 ve \u00e7a\u011fr\u0131lmas\u0131 gereken parametreleri i\u00e7eren yap\u0131land\u0131r\u0131lm\u0131\u015f bir \u00e7\u0131kt\u0131 (\u00f6rne\u011fin, JSON) \u00fcretir. LLM bu arac\u0131 do\u011frudan \u00e7a\u011f\u0131rmaz, sadece \"\u00e7a\u011fr\u0131lmas\u0131 gerekti\u011fini\" belirtir.<\/li>\n
- Orkestrasyon Katman\u0131:<\/strong> LLM'den gelen bu \u00f6neri, d\u0131\u015f bir orkestrasyon katman\u0131 (kod) taraf\u0131ndan al\u0131n\u0131r. Bu katman, \u00f6neriyi do\u011frular, arac\u0131 ger\u00e7ekten \u00e7a\u011f\u0131r\u0131r ve \u00e7\u0131kt\u0131s\u0131n\u0131 al\u0131r.<\/li>\n
- Sonu\u00e7:<\/strong> Arac\u0131n \u00e7\u0131kt\u0131s\u0131, genellikle tekrar LLM'e veya do\u011frudan kullan\u0131c\u0131ya sunulur. E\u011fer LLM'e sunulursa, LLM bu \u00e7\u0131kt\u0131y\u0131 kullanarak nihai bir yan\u0131t olu\u015fturabilir.<\/li>\n<\/ol>\n
\u00d6rnek bir Tool Calling \u00e7\u0131kt\u0131s\u0131:<\/p>\n
\nHuman: Yar\u0131nki toplant\u0131m\u0131 saat 15:00'e ertele.\nLLM Output (JSON):\n{\n \"tool_calls\": [\n {\n \"function\": {\n \"name\": \"reschedule_meeting\",\n \"arguments\": {\n \"meeting_id\": \"12345\",\n \"new_time\": \"2024-07-25T15:00:00\"\n }\n }\n }\n ]\n}\n<\/pre>\n<\/code><\/p>\n
Tool Calling'in Avantajlar\u0131 ve Dezavantajlar\u0131<\/h4>\n\n- Avantajlar\u0131:<\/strong> Daha basit ve do\u011frudan bir entegrasyon sunar. Genellikle daha az token t\u00fcketimi ve daha d\u00fc\u015f\u00fck gecikme s\u00fcresi sa\u011flar. LLM'in sadece karar verme rol\u00fcn\u00fc vurgular, y\u00fcr\u00fctme kontrol\u00fcn\u00fc d\u0131\u015f sisteme b\u0131rak\u0131r, bu da g\u00fcvenlik a\u00e7\u0131s\u0131ndan \u00f6nemlidir.<\/li>\n
- Dezavantajlar\u0131:<\/strong> \u00c7ok ad\u0131ml\u0131, karma\u015f\u0131k g\u00f6revler i\u00e7in ReAct kadar esnek de\u011fildir; her ad\u0131mda LLM'e tekrar sorgu g\u00f6ndermek gerekebilir. LLM'in d\u00fc\u015f\u00fcnce s\u00fcreci d\u0131\u015far\u0131dan daha az g\u00f6r\u00fcn\u00fcrd\u00fcr.<\/li>\n<\/ul>\n
ReAct ve Tool Calling Aras\u0131ndaki Temel Farklar<\/h3>\n
Her iki yakla\u015f\u0131m da LLM'lerin d\u0131\u015f ara\u00e7larla etkile\u015fim kurmas\u0131n\u0131 sa\u011flarken, temel felsefeleri ve uygulama alanlar\u0131 a\u00e7\u0131s\u0131ndan \u00f6nemli farkl\u0131l\u0131klar g\u00f6sterirler.<\/p>\n
Karar Mekanizmas\u0131<\/h4>\n
ReAct'te LLM, her ad\u0131mda hem ne d\u00fc\u015f\u00fcnd\u00fc\u011f\u00fcn\u00fc (ak\u0131l y\u00fcr\u00fctme) hem de hangi eylemi ger\u00e7ekle\u015ftirece\u011fini (eylem) a\u00e7\u0131k\u00e7a belirtir. Bu, daha dinamik ve ba\u011flama duyarl\u0131 bir karar verme s\u00fcrecidir. Tool Calling'de ise LLM, genellikle tek bir ad\u0131mda, kullan\u0131c\u0131n\u0131n iste\u011fine g\u00f6re do\u011frudan bir ara\u00e7 \u00e7a\u011fr\u0131s\u0131 \u00f6nerisi \u00fcretir. Ak\u0131l y\u00fcr\u00fctme s\u00fcreci, ReAct'teki kadar a\u00e7\u0131k\u00e7a d\u0131\u015fa vurulmaz.<\/p>\n
Esneklik ve Karma\u015f\u0131kl\u0131k<\/h4>\n
ReAct, daha karma\u015f\u0131k ve \u00e7ok ad\u0131ml\u0131 g\u00f6revler i\u00e7in do\u011fal bir uyum sa\u011flar \u00e7\u00fcnk\u00fc LLM, g\u00f6rev boyunca s\u00fcrekli olarak \u00f6\u011frenip adapte olabilir. Tool Calling ise daha \u00e7ok tekil, iyi tan\u0131mlanm\u0131\u015f eylemler veya bilgi alma g\u00f6revleri i\u00e7in idealdir. \u00c7ok ad\u0131ml\u0131 bir s\u00fcre\u00e7 gerekti\u011finde, Tool Calling ile her ad\u0131m i\u00e7in ayr\u0131 bir LLM \u00e7a\u011fr\u0131s\u0131 ve orkestrasyon gerekebilir.<\/p>\n
Uygulama Alanlar\u0131<\/h4>\n\n- ReAct:<\/strong> Otonom ajanlar, uzun soluklu diyalog sistemleri, karma\u015f\u0131k problem \u00e7\u00f6zme, ara\u015ft\u0131rma ve geli\u015ftirme g\u00f6revleri.<\/li>\n
- Tool Calling:<\/strong> Chatbot'lar, veri alma API'leri, basit otomasyon g\u00f6revleri, form doldurma, takvim y\u00f6netimi gibi belirli i\u015flevlere odaklanan uygulamalar.<\/li>\n<\/ul>\n
Bir Kar\u015f\u0131la\u015ft\u0131rma Tablosu<\/h4>\n
\n\n\n\u00d6zellik<\/th>\n ReAct<\/th>\n Tool Calling<\/th>\n<\/tr>\n<\/thead>\n \n\nAk\u0131l Y\u00fcr\u00fctme<\/strong><\/td>\nD\u00f6ng\u00fcsel, a\u00e7\u0131k ve dinamik<\/td>\n Tek seferlik, daha kapal\u0131<\/td>\n<\/tr>\n \nEsneklik<\/strong><\/td>\nY\u00fcksek (\u00e7ok ad\u0131ml\u0131 g\u00f6revler)<\/td>\n Orta (tekil veya basit ad\u0131mlar)<\/td>\n<\/tr>\n \nToken T\u00fcketimi<\/strong><\/td>\nGenellikle daha y\u00fcksek<\/td>\n Genellikle daha d\u00fc\u015f\u00fck<\/td>\n<\/tr>\n \nGecikme S\u00fcresi<\/strong><\/td>\nDaha y\u00fcksek<\/td>\n Daha d\u00fc\u015f\u00fck<\/td>\n<\/tr>\n \nG\u00fcvenlik Kontrol\u00fc<\/strong><\/td>\nLLM'in d\u00fc\u015f\u00fcnce ad\u0131mlar\u0131 incelenebilir<\/td>\n LLM sadece \u00f6neri sunar, d\u0131\u015f sistem y\u00fcr\u00fct\u00fcr<\/td>\n<\/tr>\n \nUygulama Alan\u0131<\/strong><\/td>\nOtonom ajanlar, karma\u015f\u0131k g\u00f6revler<\/td>\n Chatbot'lar, API entegrasyonlar\u0131<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nNeden LLM Karar Vermeli, Asla Y\u00fcr\u00fctmemeli?<\/h3>\n
Bu, LLM entegrasyonlar\u0131n\u0131n en kritik g\u00fcvenlik ve kontrol prensibidir. B\u00fcy\u00fck dil modelleri, ara\u00e7lar\u0131 ne zaman ve nas\u0131l kullanacaklar\u0131na dair ak\u0131l y\u00fcr\u00fctme ve karar verme konusunda olduk\u00e7a yeteneklidir. Ancak, bu kararlar\u0131n ger\u00e7ek d\u00fcnyadaki etkilerini do\u011frudan y\u00fcr\u00fctme yetkisinin LLM'lere verilmemesi esast\u0131r.<\/p>\n
G\u00fcvenlik Endi\u015feleri<\/h4>\n
LLM'ler hal\u00fcsinasyon g\u00f6rebilir, yanl\u0131\u015f anlayabilir veya k\u00f6t\u00fc niyetli girdilere maruz kalabilir. E\u011fer bir LLM do\u011frudan bir arac\u0131 (\u00f6rne\u011fin, bir veritaban\u0131 silme arac\u0131 veya bir finansal i\u015flem arac\u0131) y\u00fcr\u00fctme yetkisine sahip olsayd\u0131, bu durum felaketle sonu\u00e7lanabilecek hatalara veya g\u00fcvenlik ihlallerine yol a\u00e7abilirdi. LLM'in sadece bir eylem \u00f6nermesi, bu \u00f6nerinin bir insan veya ba\u015fka bir g\u00fcvenli sistem taraf\u0131ndan do\u011frulanmas\u0131na olanak tan\u0131r.<\/p>\n
Kontrol ve G\u00f6zetim<\/h4>\n
LLM'lerin eylemlerini do\u011frudan y\u00fcr\u00fctmek yerine, bir orkestrasyon katman\u0131n\u0131n (insan veya kod) arac\u0131 olarak kullan\u0131lmas\u0131, sistem \u00fczerinde tam kontrol sa\u011flar. Bu katman, LLM'in \u00f6nerilerini filtreleyebilir, onaylayabilir veya reddedebilir. Bu, \u00f6zellikle kritik i\u015f s\u00fcre\u00e7lerinde veya hassas verilerle \u00e7al\u0131\u015f\u0131rken hayati \u00f6neme sahiptir.<\/p>\n
Hata Y\u00f6netimi ve Geri Alma<\/h4>\n
LLM'ler hata yapmaya meyillidir. E\u011fer bir LLM do\u011frudan bir eylemi y\u00fcr\u00fctseydi ve bu eylem yanl\u0131\u015f olsayd\u0131, geri alma s\u00fcreci karma\u015f\u0131k veya imkans\u0131z olabilirdi. Bir orkestrasyon katman\u0131, arac\u0131n \u00e7\u0131kt\u0131s\u0131n\u0131 de\u011ferlendirebilir, hatalar\u0131 tespit edebilir ve gerekirse geri alma mekanizmalar\u0131n\u0131 tetikleyebilir veya LLM'e geri bildirim sa\u011flayarak d\u00fczeltme yapmas\u0131n\u0131 isteyebilir.<\/p>\n
\u015eeffafl\u0131k ve Denetlenebilirlik<\/h4>\n
LLM'in sadece karar vermesi ve bir eylem \u00f6nermesi, sistemin \u015feffafl\u0131\u011f\u0131n\u0131 art\u0131r\u0131r. Hangi karar\u0131n neden verildi\u011fi, hangi arac\u0131n \u00e7a\u011fr\u0131lmak istendi\u011fi ve hangi parametrelerle \u00e7a\u011fr\u0131laca\u011f\u0131 a\u00e7\u0131k\u00e7a g\u00f6r\u00fclebilir. Bu, denetlenebilirlik ve uyumluluk a\u00e7\u0131s\u0131ndan kritik \u00f6neme sahiptir, \u00f6zellikle d\u00fczenlemeye tabi sekt\u00f6rlerde.<\/p>\n
Pratikte Uygulama: En \u0130yi Yakla\u015f\u0131m<\/h3>\n
Hem ReAct hem de Tool Calling'in g\u00fc\u00e7l\u00fc y\u00f6nleri vard\u0131r. En iyi uygulama, genellikle bu yakla\u015f\u0131mlar\u0131 hibrit bir \u015fekilde, sa\u011flam bir orkestrasyon katman\u0131 ile birle\u015ftirmektir.<\/p>\n
Orkestrasyon Katman\u0131<\/h4>\n
Her iki modelde de, LLM'in \u00e7\u0131kt\u0131s\u0131n\u0131 alan ve ger\u00e7ek d\u00fcnya eylemlerini y\u00f6neten bir \"orkestrasyon katman\u0131\" hayati \u00f6neme sahiptir. Bu katman \u015funlar\u0131 yapmal\u0131d\u0131r:<\/p>\n
\n- LLM'den gelen ara\u00e7 \u00e7a\u011fr\u0131s\u0131 \u00f6nerilerini (ReAct'teki eylemler veya Tool Calling'deki JSON \u00e7\u0131kt\u0131lar\u0131) do\u011frulamak.<\/li>\n
- Gerekirse kullan\u0131c\u0131dan veya bir y\u00f6neticiden onay almak.<\/li>\n
- Ara\u00e7lar\u0131 g\u00fcvenli bir \u015fekilde \u00e7a\u011f\u0131rmak ve hatalar\u0131 y\u00f6netmek.<\/li>\n
- Ara\u00e7 \u00e7\u0131kt\u0131s\u0131n\u0131 LLM'e geri beslemek (ReAct i\u00e7in) veya nihai yan\u0131t\u0131 olu\u015fturmak.<\/li>\n<\/ul>\n
Bu katman, Python'da LangChain, LlamaIndex gibi k\u00fct\u00fcphanelerle veya \u00f6zel bir API gateway\/servis mimarisiyle olu\u015fturulabilir.<\/p>\n
\u0130nsan G\u00f6zetiminin \u00d6nemi<\/h4>\n
\u00d6zellikle kritik veya geri al\u0131namaz eylemler i\u00e7in insan d\u00f6ng\u00fcde (Human-in-the-loop) bir onay mekanizmas\u0131 bulundurmak, g\u00fcvenlik ve do\u011fruluk a\u00e7\u0131s\u0131ndan en sa\u011flam yakla\u015f\u0131md\u0131r. LLM, bir eylem \u00f6nerebilir, ancak bu eylem bir insan taraf\u0131ndan incelenip onaylanmadan y\u00fcr\u00fct\u00fclmez.<\/p>\n
Mimari Tasar\u0131m \u0130pu\u00e7lar\u0131<\/h4>\n\n- Mod\u00fcler Ara\u00e7lar:<\/strong> LLM'e sunulan ara\u00e7lar\u0131n iyi tan\u0131mlanm\u0131\u015f, atomik ve g\u00fcvenli oldu\u011fundan emin olun. Her arac\u0131n tek bir i\u015flevi olmal\u0131 ve yan etkileri \u00f6ng\u00f6r\u00fclebilir olmal\u0131d\u0131r.<\/li>\n
- Yetkilendirme ve Kimlik Do\u011frulama:<\/strong> Ara\u00e7lar\u0131n \u00e7a\u011fr\u0131ld\u0131\u011f\u0131 sistemlerde uygun yetkilendirme ve kimlik do\u011frulama mekanizmalar\u0131 uygulay\u0131n. LLM'in kendisi bu yetkilendirmeyi yapmamal\u0131d\u0131r.<\/li>\n
- Geri Bildirim D\u00f6ng\u00fcleri:<\/strong> LLM'in performans\u0131n\u0131 art\u0131rmak i\u00e7in ara\u00e7 \u00e7a\u011fr\u0131lar\u0131n\u0131n sonu\u00e7lar\u0131n\u0131 (ba\u015far\u0131\/ba\u015far\u0131s\u0131zl\u0131k, do\u011fru\/yanl\u0131\u015f) LLM'e geri besleyen mekanizmalar kurun.<\/li>\n
- G\u00fcnl\u00fckleme ve \u0130zleme:<\/strong> T\u00fcm LLM kararlar\u0131n\u0131 ve ara\u00e7 \u00e7a\u011fr\u0131lar\u0131n\u0131 detayl\u0131 bir \u015fekilde g\u00fcnl\u00fc\u011fe kaydedin. Bu, hata ay\u0131klama, denetim ve sistemin davran\u0131\u015f\u0131n\u0131 anlama a\u00e7\u0131s\u0131ndan kritik \u00f6neme sahiptir.<\/li>\n<\/ul>\n
Sonu\u00e7<\/h3>\n
ReAct ve Tool Calling, B\u00fcy\u00fck Dil Modellerinin d\u0131\u015f d\u00fcnyayla etkile\u015fim kurmas\u0131n\u0131 sa\u011flayan g\u00fc\u00e7l\u00fc paradigmalar sunar. ReAct, karma\u015f\u0131k, \u00e7ok ad\u0131ml\u0131 ak\u0131l y\u00fcr\u00fctme ve eylem senaryolar\u0131 i\u00e7in ideal bir esneklik sunarken, Tool Calling daha do\u011frudan ve maliyet etkin bir ara\u00e7 \u00e7a\u011f\u0131rma mekanizmas\u0131 sa\u011flar. Hangi yakla\u015f\u0131m benimsenirse benimsensin, temel prensip ayn\u0131 kalmal\u0131d\u0131r: LLM'ler karar verme, planlama ve hangi arac\u0131n kullan\u0131laca\u011f\u0131na dair \u00f6nerilerde bulunma konusunda m\u00fckemmeldir. Ancak, ger\u00e7ek d\u00fcnya eylemlerini do\u011frudan y\u00fcr\u00fctme yetkisi asla LLM'lere verilmemelidir. Bunun yerine, bu kararlar\u0131n g\u00fcvenli, kontrol edilebilir ve denetlenebilir bir orkestrasyon katman\u0131 arac\u0131l\u0131\u011f\u0131yla, m\u00fcmk\u00fcnse insan g\u00f6zetiminde y\u00fcr\u00fct\u00fclmesi, yapay zeka sistemlerimizin g\u00fcvenli\u011fini, g\u00fcvenilirli\u011fini ve sorumlulu\u011funu sa\u011flaman\u0131n anahtar\u0131d\u0131r.<\/p>\n
SSS (S\u0131k Sorulan Sorular)<\/h3>\nS1: ReAct mi, Tool Calling mi daha iyidir?<\/h4>\n
C1: \"Daha iyi\" diye bir kavram yoktur; her ikisinin de farkl\u0131 kullan\u0131m durumlar\u0131 i\u00e7in avantajlar\u0131 vard\u0131r. ReAct, karma\u015f\u0131k, \u00e7ok ad\u0131ml\u0131 g\u00f6revler ve dinamik ak\u0131l y\u00fcr\u00fctme gerektiren durumlar i\u00e7in daha uygundur. Tool Calling ise daha basit, tekil ara\u00e7 \u00e7a\u011fr\u0131lar\u0131 ve daha d\u00fc\u015f\u00fck maliyet\/gecikme s\u00fcresi gerektiren senaryolar i\u00e7in idealdir.<\/p>\n
S2: LLM'in do\u011frudan eylem y\u00fcr\u00fctmesi neden tehlikelidir?<\/h4>\n
C2: LLM'ler hal\u00fcsinasyon g\u00f6rebilir, yanl\u0131\u015f anlayabilir veya k\u00f6t\u00fc niyetli girdilere maruz kalabilir. Do\u011frudan eylem yetkisi, yanl\u0131\u015fl\u0131kla veri silme, finansal i\u015flemler yapma veya kritik sistemlere zarar verme gibi felaketle sonu\u00e7lanabilecek hatalara yol a\u00e7abilir. G\u00fcvenlik, kontrol ve hata y\u00f6netimi a\u00e7\u0131s\u0131ndan LLM'in sadece karar vermesi ve bir d\u0131\u015f sistemin y\u00fcr\u00fctmesi en g\u00fcvenli yakla\u015f\u0131md\u0131r.<\/p>\n
S3: Orkestrasyon katman\u0131 ne i\u015fe yarar?<\/h4>\n
C3: Orkestrasyon katman\u0131, LLM'den gelen ara\u00e7 \u00e7a\u011fr\u0131s\u0131 \u00f6nerilerini al\u0131r, bunlar\u0131 do\u011frular, ger\u00e7ek ara\u00e7lar\u0131 g\u00fcvenli bir \u015fekilde \u00e7a\u011f\u0131r\u0131r, \u00e7\u0131kt\u0131lar\u0131n\u0131 y\u00f6netir ve gerekirse insan onay\u0131 al\u0131r. Bu katman, LLM ile ger\u00e7ek d\u00fcnya eylemleri aras\u0131nda bir g\u00fcvenlik ve kontrol tamponu g\u00f6revi g\u00f6r\u00fcr.<\/p>\n
S4: \u0130nsan g\u00f6zetimi her zaman gerekli midir?<\/h4>\n
C4: Her zaman gerekli olmayabilir, ancak kritik, geri al\u0131namaz veya y\u00fcksek riskli eylemler i\u00e7in kesinlikle tavsiye edilir. Daha d\u00fc\u015f\u00fck riskli, rutin g\u00f6revlerde, iyi test edilmi\u015f ve g\u00fcvenli bir orkestrasyon katman\u0131 yeterli olabilir. Ancak, sistemin ilk a\u015famalar\u0131nda veya beklenmedik durumlar i\u00e7in insan d\u00f6ng\u00fcde olmak her zaman iyi bir uygulamad\u0131r.<\/p>\n","protected":false},"excerpt":{"rendered":"ReAct, “Reasoning” (Ak\u0131l Y\u00fcr\u00fctme) ve “Acting” (Eyleme Ge\u00e7me) kelimelerinin birle\u015fimiyle olu\u015fan, LLM’lerin karma\u015f\u0131k g\u00f6revleri yerine getirirken hem a…","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":[128],"tags":[],"class_list":{"0":"post-36927","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-react","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\n
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