Bu komut, modeli Grok gibi davranmaya y\u00f6nlendirir; Grok, \u00e7o\u011fu veri setinin aksi ispatlanana kadar \u00e7\u00f6p oldu\u011funa inanan, son derece d\u00fcr\u00fcst ve \u015f\u00fcpheci bir veri denet\u00e7isidir. Model, sa\u011flanan her veri setini, verilerin temsil etti\u011fini iddia etti\u011fi \u015feyle ger\u00e7ekte yakalad\u0131\u011f\u0131 \u015feyi kar\u015f\u0131la\u015ft\u0131rarak incelemeli ve eksik ba\u011flam, de\u011fi\u015fen tan\u0131mlar, hatal\u0131 zaman damgalar\u0131, tekrarlar, \u00f6nyarg\u0131lar ve anlams\u0131z \u00f6l\u00e7\u00fcmler gibi yap\u0131sal kusurlar\u0131 ortaya \u00e7\u0131karmal\u0131d\u0131r. Model, her sorunu ciddiyetine g\u00f6re (\u00f6l\u00fcmc\u00fcl, ciddi, rahats\u0131z edici) s\u0131n\u0131fland\u0131rmal\u0131 ve hangi verilerin ger\u00e7ekten kararlar\u0131 destekleyebilece\u011fini ve hangilerinin tamamen g\u00f6z ard\u0131 edilmesi veya silinmesi gerekti\u011fini belirlemek i\u00e7in bir "kullan\u0131\u015fl\u0131l\u0131k testi" yapmal\u0131d\u0131r. Model, bozuk verilerin yaratt\u0131\u011f\u0131 yanl\u0131\u015f g\u00fcveni vurgulamal\u0131, k\u00f6t\u00fc girdilerin nas\u0131l yanl\u0131\u015f kararlara yol a\u00e7abilece\u011fini a\u00e7\u0131klamal\u0131 ve g\u00fcvenilir verilerin k\u00fc\u00e7\u00fck bir alt k\u00fcmesini belirlemelidir. Son olarak, neyin d\u00fczeltilmesi, neyin s\u0131f\u0131rdan toplanmas\u0131 ve neyin kal\u0131c\u0131 olarak at\u0131lmas\u0131 gerekti\u011fini \u00f6nermeli ve veri ger\u00e7ekli\u011finin a\u00e7\u0131k ve son derece d\u00fcr\u00fcst bir \u00f6zetini sunmal\u0131d\u0131r.<\/p>","protected":false},"excerpt":{"rendered":"
This prompt instructs the model to act as Grok \u2014 a brutally honest, skeptical data auditor whose default belief is that most datasets are garbage until proven otherwise. The model must dissect any provided dataset by comparing what the data claims to represent with what it actually captures, while exposing structural flaws like missing context, […]<\/p>\n","protected":false},"featured_media":25529,"comment_status":"open","ping_status":"closed","template":"","meta":[],"product_brand":[],"product_cat":[4094],"product_tag":[],"class_list":{"0":"post-26519","1":"product","2":"type-product","3":"status-publish","4":"has-post-thumbnail","6":"product_cat-business-marketing","8":"first","9":"instock","10":"downloadable","11":"virtual","12":"product-type-simple"},"bundled_by":["27552"],"bundle_stock_status":"instock","bundle_stock_quantity":null,"bundle_virtual":false,"bundle_layout":"","bundle_add_to_cart_form_location":"","bundle_editable_in_cart":false,"bundle_sold_individually_context":"","bundle_item_grouping":"","bundle_min_size":"","bundle_max_size":"","bundle_price":[],"bundled_items":[],"bundle_sell_ids":[],"_links":{"self":[{"href":"https:\/\/logic-vault.com\/tr\/wp-json\/wp\/v2\/product\/26519","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/logic-vault.com\/tr\/wp-json\/wp\/v2\/product"}],"about":[{"href":"https:\/\/logic-vault.com\/tr\/wp-json\/wp\/v2\/types\/product"}],"replies":[{"embeddable":true,"href":"https:\/\/logic-vault.com\/tr\/wp-json\/wp\/v2\/comments?post=26519"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/logic-vault.com\/tr\/wp-json\/wp\/v2\/media\/25529"}],"wp:attachment":[{"href":"https:\/\/logic-vault.com\/tr\/wp-json\/wp\/v2\/media?parent=26519"}],"wp:term":[{"taxonomy":"product_brand","embeddable":true,"href":"https:\/\/logic-vault.com\/tr\/wp-json\/wp\/v2\/product_brand?post=26519"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/logic-vault.com\/tr\/wp-json\/wp\/v2\/product_cat?post=26519"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/logic-vault.com\/tr\/wp-json\/wp\/v2\/product_tag?post=26519"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}