{"id":1407,"date":"2026-02-27T14:20:00","date_gmt":"2026-02-27T13:20:00","guid":{"rendered":"https:\/\/jsoncrew.com\/?p=1407"},"modified":"2026-05-07T17:54:14","modified_gmt":"2026-05-07T15:54:14","slug":"architektury-ai-w-firmie-od-czatu-do-rag","status":"publish","type":"post","link":"https:\/\/jsoncrew.com\/en\/2026\/02\/27\/architektury-ai-w-firmie-od-czatu-do-rag\/","title":{"rendered":"AI Architectures in Companies \u2013 From Chatbots to RAG and Agents"},"content":{"rendered":"<p><em>Ten tekst odpowiada na pytania, kt\u00f3re i tak zadasz przy rozmowie z IT lub dostawc\u0105: czym si\u0119 r\u00f3\u017cni \u201ezwyk\u0142y ChatGPT\u201d od systemu na dokumentach firmy, ile to kosztuje i po co w og\u00f3le rozr\u00f3\u017cnia\u0107 architektury.<\/em><\/p>\n<p>Zanim podpiszesz bud\u017cet na \u201ewdro\u017cenie AI\u201d, warto wiedzie\u0107 jedno: <strong>\u201eAI\u201d to nie jeden produkt<\/strong>. Here are a few <strong>architecture<\/strong> &#8211; od prostego czatu po system z ca\u0142ym archiwum i automatyzacj\u0119. Poni\u017cej: <strong>co daje ka\u017cda, czego nie daje, ile kosztuje<\/strong> &#8211; bez skr\u00f3t\u00f3w typu \u201ezaufaj nam, zrobimy AI\u201d.<\/p>\n<hr>\n<h2>Po co Ci to wiedzie\u0107 przed decyzj\u0105?<\/h2>\n<p><!-- jsoncrew-inline-cta --><\/p>\n<div class=\"jsoncrew-inline-cta\" style=\"margin:2.5rem 0;padding:1.75rem 1.5rem;background:linear-gradient(135deg,#020C08,#0a3a28);border:1px solid rgba(1,108,71,.4);border-left:4px solid #016C47;border-radius:10px;color:#e7ebe9;\">\n<div style=\"font-size:11px;text-transform:uppercase;letter-spacing:.14em;color:#0a9b68;font-weight:700;margin-bottom:.5rem;\">NEWSLETTER<\/div>\n<h3 style=\"font-size:1.3rem;margin:0 0 .75rem;color:#fff;line-height:1.35;\">Co tydzie\u0144 1 mail z konkretem<\/h3>\n<p style=\"margin:0 0 1.25rem;color:#a8b1ad;font-size:.95rem;line-height:1.5;\">O AI, sprzeda\u017cy B2B i wdro\u017ceniach. Bez spamu, wypisujesz si\u0119 jednym klikiem.<\/p>\n<p>  <a href=\"\/kontakt\/?utm_source=blog&#038;utm_medium=inline_cta&#038;utm_campaign=architektury-ai-w-firmie-od-czatu-do-rag\" style=\"display:inline-block;padding:.85rem 1.5rem;background:linear-gradient(135deg,#016C47,#0a9b68);color:#fff;text-decoration:none;border-radius:8px;font-weight:600;font-size:.95rem;box-shadow:0 4px 12px rgba(1,108,71,.3);\">Zapisz si\u0119 \u2192<\/a>\n<\/div>\n<p>Bo wyb\u00f3r architektury ustala:<\/p>\n<ul>\n<li><strong>What the system actually does<\/strong> &#8211; tylko odpowiada, czy te\u017c korzysta z Twoich dokument\u00f3w i pokazuje \u017ar\u00f3d\u0142a?<\/li>\n<li><strong>Cost scale<\/strong> &#8211; rz\u0105d wielko\u015bci od kilkudziesi\u0119ciu euro miesi\u0119cznie po dziesi\u0105tki tysi\u0119cy euro rocznie.<\/li>\n<li><strong>Bezpiecze\u0144stwo danych<\/strong> &#8211; czy tre\u015b\u0107 opuszcza firm\u0119 w kontrolowany spos\u00f3b, czy zostaje w wybranym regionie.<\/li>\n<li><strong>Start time<\/strong> &#8211; od jednego dnia po kilka miesi\u0119cy.<\/li>\n<\/ul>\n<p>Nie musisz zna\u0107 sieci neuronowych. Musisz umie\u0107 powiedzie\u0107: <strong>\u201ePotrzebujemy architektury X, bo mamy problem Y\u201d<\/strong> &#8211; albo zada\u0107 dostawcy trudne pytanie. W tym pomo\u017ce te\u017c <a href=\"https:\/\/jsoncrew.com\/?p=1402\">szerszy przewodnik po narz\u0119dziach i kosztach<\/a>.<\/p>\n<hr>\n<h2>1. Prosty chat AI (pytanie \u2192 odpowied\u017a)<\/h2>\n<p><strong>Jak to dzia\u0142a:<\/strong> Wpisujesz pytanie. Model odpowiada na podstawie treningu &#8211; bez sta\u0142ego dost\u0119pu do Twoich plik\u00f3w.<\/p>\n<pre><code>Twoje pytanie \u2192 [model] \u2192 odpowied\u017a\n<\/code><\/pre>\n<p><strong>Przyk\u0142ady:<\/strong> ChatGPT, Claude, and Gemini in basic mode.<\/p>\n<p><strong>What it can do:<\/strong> pisa\u0107, t\u0142umaczy\u0107, streszcza\u0107, pomaga\u0107 przy kodzie, liczbach, formatowaniu; przeanalizowa\u0107 <strong>excerpt<\/strong>, kt\u00f3ry wkleisz w oknie.<\/p>\n<p><strong>Czego nie potrafi (m\u00f3wimy wprost):<\/strong> nie zna ca\u0142ego archiwum firmy; nie przeszukuje dysku; pami\u0119\u0107 mi\u0119dzy sesjami bywa ograniczona lub \u017cadna; <strong>mo\u017ce halucynowa\u0107<\/strong> &#8211; brzmi m\u0105drze, a bywa nieprawd\u0105.<\/p>\n<p><strong>Analogy<\/strong> Rozmowa z bardzo og\u00f3lnie \u201em\u0105dr\u0105\u201d osob\u0105, kt\u00f3ra <strong>nigdy nie widzia\u0142a Twoich um\u00f3w<\/strong>.<\/p>\n<p><strong>Koszt (rz\u0105d wielko\u015bci):<\/strong> ok. 0\u201330 EUR\/osob\u0119\/miesi\u0105c.<\/p>\n<p><strong>When it&#x27;s enough:<\/strong> pisanie, t\u0142umaczenia, burza m\u00f3zg\u00f3w &#8211; <strong>without<\/strong> potrzeby przeszukiwania setek dokument\u00f3w firmy.<\/p>\n<hr>\n<h2>2. Chat z kontekstem (wgrywanie plik\u00f3w)<\/h2>\n<p><strong>Jak to dzia\u0142a:<\/strong> Do rozmowy do\u0142\u0105czasz plik (PDF, DOCX). Model odpowiada <strong>o tre\u015bci tego pliku<\/strong> during the session.<\/p>\n<pre><code>Pytanie + wgrany plik \u2192 [model] \u2192 odpowied\u017a o tym pliku\n<\/code><\/pre>\n<p><strong>Przyk\u0142ady:<\/strong> ChatGPT z za\u0142\u0105cznikiem, Claude z dokumentami, Copilot w Wordzie.<\/p>\n<p><strong>Co potrafi wi\u0119cej ni\u017c prosty chat:<\/strong> analiza konkretnej umowy, por\u00f3wnanie kilku wgranych plik\u00f3w, streszczenia.<\/p>\n<p><strong>What it can&#x27;t do:<\/strong> does not index <strong>ca\u0142ej<\/strong> company database – only what you upload <strong>now<\/strong>; limity rozmiaru i liczby plik\u00f3w; <strong>brak trwa\u0142ej \u201epami\u0119ci firmy\u201d<\/strong> per dokument; s\u0142aba kontrola dost\u0119pu (kto mo\u017ce widzie\u0107 czyje dane).<\/p>\n<p><strong>Analogy<\/strong> Kto\u015b dostaje <strong>jedn\u0105 teczk\u0119<\/strong> &#8211; przeczyta i odpowie, ale nie ma klucza do ca\u0142ego archiwum.<\/p>\n<p><strong>Cost:<\/strong> ok. 20\u201350 EUR\/osob\u0119\/miesi\u0105c.<\/p>\n<p><strong>When it&#x27;s enough:<\/strong> praca na pojedynczych dokumentach, bez potrzeby przeszukiwania setek plik\u00f3w.<\/p>\n<hr>\n<h2>3. RAG &#8211; dost\u0119p do bazy wiedzy firmy<\/h2>\n<p><strong>Jak to dzia\u0142a:<\/strong> Dokumenty trafiaj\u0105 do bazy. Na pytanie system <strong>first searches for<\/strong> relevant excerpts, then the model <strong>buduje odpowied\u017a na ich podstawie<\/strong>, cz\u0119sto z przypisami do \u017ar\u00f3de\u0142.<\/p>\n<pre><code>Pytanie \u2192 wyszukiwanie w dokumentach \u2192 fragmenty \u2192 [model] \u2192 odpowied\u017a + przypisy\n<\/code><\/pre>\n<p><strong>Kluczowa r\u00f3\u017cnica:<\/strong> model nie \u201ezgaduje z g\u0142owy\u201d w kwestiach Twojej firmy &#8211; ma <strong>konkretne cytaty z Twoich plik\u00f3w<\/strong>.<\/p>\n<p><strong>What it can do:<\/strong> przeszukiwanie du\u017cej liczby dokument\u00f3w; <strong>Footnotes<\/strong> (dokument, strona); baza ro\u015bnie wraz z nowymi plikami; \u0142\u0105czenie informacji z wielu \u017ar\u00f3de\u0142 w jednej odpowiedzi.<\/p>\n<p><strong>What RAG itself usually doesn&#x27;t do:<\/strong> nie wykonuje automatycznie krok\u00f3w w zewn\u0119trznych systemach (mail, CRM) bez rozszerze\u0144; jako\u015b\u0107 = jako\u015b\u0107 dokument\u00f3w i konfiguracji; <strong>b\u0142\u0119dy nadal mo\u017cliwe<\/strong> – verification remains.<\/p>\n<p><strong>Analogy<\/strong> Asystent, kt\u00f3ry <strong>przerobi\u0142 archiwum<\/strong> i przy ka\u017cdej odpowiedzi wskazuje: \u201eto jest z pliku X\u201d.<\/p>\n<p><strong>Cost:<\/strong> infrastruktura i utrzymanie cz\u0119sto <strong>setki\u2013tysi\u0105ce EUR miesi\u0119cznie<\/strong> plus wdro\u017cenie (od kilku do kilkudziesi\u0119ciu tysi\u0119cy EUR) &#8211; zale\u017cnie od skali.<\/p>\n<p><strong>When does this make sense:<\/strong> du\u017co dokument\u00f3w, wiele os\u00f3b, bran\u017ce, gdzie licz\u0105 si\u0119 <strong>\u017ar\u00f3d\u0142a i audyt<\/strong> (law, finance, medicine, HR, consulting).<\/p>\n<p>Je\u015bli dopiero testujesz hipotez\u0119 \u201eczy nam si\u0119 to op\u0142aca\u201d, sensowna jest logika <a href=\"https:\/\/jsoncrew.com\/2026\/02\/13\/co-to-jest-mvp\/\">MVP<\/a> &#8211; ma\u0142y zakres, potem skalowanie.<\/p>\n<hr>\n<h2>4. Agenci AI &#8211; wykonanie wielu krok\u00f3w<\/h2>\n<p><strong>Jak to dzia\u0142a:<\/strong> A single command runs <strong>plan<\/strong>: searching the database, checking the calendar, drafting a document, preparing it for approval, etc.<\/p>\n<p><strong>Co potrafi wi\u0119cej ni\u017c sam RAG:<\/strong> scenariusze wieloetapowe, integracje (mail, kalendarz, CRM &#8211; w granicach projektu), automatyzacja powtarzalnych proces\u00f3w.<\/p>\n<p><strong>Risks:<\/strong> b\u0142\u0105d agenta mo\u017ce mie\u0107 <strong>wi\u0119ksze skutki<\/strong> ni\u017c b\u0142\u0105d samego czatu; trzeba jasno okre\u015bli\u0107 <strong>what&#x27;s allowed, what isn&#x27;t<\/strong>; wdro\u017cenie i testy trwaj\u0105 d\u0142u\u017cej.<\/p>\n<p><strong>Analogy<\/strong> RAG is like a librarian with a catalog; <strong>agent<\/strong> jak asystent, kt\u00f3ry co\u015b <strong>za\u0142atwia<\/strong> &#8211; pod Twoim nadzorem i regu\u0142ami.<\/p>\n<p><strong>Cost:<\/strong> cz\u0119sto <strong>tysi\u0105ce EUR miesi\u0119cznie<\/strong> + wdro\u017cenie <strong>dziesi\u0105tki tysi\u0119cy EUR<\/strong> w zale\u017cno\u015bci od z\u0142o\u017cono\u015bci.<\/p>\n<p><strong>When:<\/strong> usually <strong>po<\/strong> stabilnym RAG lub jasno zdefiniowanej bazie wiedzy; cz\u0119sto \u0142\u0105czy si\u0119 z porz\u0105dkiem w <a href=\"https:\/\/jsoncrew.com\/2026\/01\/31\/sprzedaz-b2b-nie-jest-wolna-przez-ludzi-jest-wolna-przez-proces\/\">procesie sprzeda\u017cy i operacji<\/a>.<\/p>\n<hr>\n<h2>5. Fine-tuning &#8211; model \u201edouczony\u201d na Twoich danych<\/h2>\n<p><strong>Jak to dzia\u0142a:<\/strong> The base model is <strong>tunable<\/strong> na wybranych danych firmy (styl, typowe sformu\u0142owania, domena).<\/p>\n<p><strong>R\u00f3\u017cnica wzgl\u0119dem RAG:<\/strong> wiedza jest w \u201ewagach\u201d modelu, a nie tylko w wyszukiwanej bazie; odpowiedzi mog\u0105 by\u0107 szybsze; <strong>nie zast\u0119puje<\/strong> przypis\u00f3w do konkretnych aktualnych dokument\u00f3w.<\/p>\n<p><strong>Risks and costs:<\/strong> the training is <strong>roads<\/strong>; updating knowledge requires additional cycles; without RAG <strong>trudniej pokaza\u0107 \u017ar\u00f3d\u0142o<\/strong> jak w przypadku cytatu z PDF; potrzeba du\u017cej ilo\u015bci <strong>jako\u015bciowych<\/strong> training data.<\/p>\n<p><strong>When:<\/strong> rzadko jako jedyne rozwi\u0105zanie; cz\u0119\u015bciej <strong>RAG + optionally<\/strong> tailoring the model where it makes business sense.<\/p>\n<hr>\n<h2>Summary table<\/h2>\n<table>\n<thead>\n<tr>\n<th>Feature<\/th>\n<th align=\"center\">A simple cabin<\/th>\n<th align=\"center\">Chat + file<\/th>\n<th align=\"center\">RAG<\/th>\n<th align=\"center\">Agents<\/th>\n<th align=\"center\">Fine-tuning<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Knows company documents<\/td>\n<td align=\"center\">No<\/td>\n<td align=\"center\">Only uploaded<\/td>\n<td align=\"center\">Yes (base)<\/td>\n<td align=\"center\">Yes<\/td>\n<td align=\"center\">\u201eWewn\u0105trz modelu\u201d<\/td>\n<\/tr>\n<tr>\n<td>Wskazuje \u017ar\u00f3d\u0142a<\/td>\n<td align=\"center\">No<\/td>\n<td align=\"center\">Cz\u0119\u015bciowo<\/td>\n<td align=\"center\">Yes<\/td>\n<td align=\"center\">Yes<\/td>\n<td align=\"center\">No<\/td>\n<\/tr>\n<tr>\n<td>Pami\u0119\u0107 bazy mi\u0119dzy sesjami<\/td>\n<td align=\"center\">No<\/td>\n<td align=\"center\">No<\/td>\n<td align=\"center\">Yes<\/td>\n<td align=\"center\">Yes<\/td>\n<td align=\"center\">Yes<\/td>\n<\/tr>\n<tr>\n<td>Wykonuje dzia\u0142ania w systemach<\/td>\n<td align=\"center\">No<\/td>\n<td align=\"center\">No<\/td>\n<td align=\"center\">Usually not*<\/td>\n<td align=\"center\">Yes<\/td>\n<td align=\"center\">No<\/td>\n<\/tr>\n<tr>\n<td>Pr\u00f3g wej\u015bcia kosztem<\/td>\n<td align=\"center\">Short<\/td>\n<td align=\"center\">Short<\/td>\n<td align=\"center\">\u015aredni\/wysoki<\/td>\n<td align=\"center\">Tall<\/td>\n<td align=\"center\">Tall<\/td>\n<\/tr>\n<tr>\n<td>Z\u0142o\u017cono\u015b\u0107 wdro\u017cenia<\/td>\n<td align=\"center\">Low<\/td>\n<td align=\"center\">Low<\/td>\n<td align=\"center\">\u015arednia<\/td>\n<td align=\"center\">Tall<\/td>\n<td align=\"center\">Tall<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>*Mo\u017cliwe rozszerzenia poza czystym RAG.<\/p>\n<hr>\n<h2>Kt\u00f3r\u0105 architektur\u0119 wybra\u0107?<\/h2>\n<p>Typowa \u015bcie\u017cka:<\/p>\n<ol>\n<li><strong>Dzi\u015b:<\/strong> prosty chat \/ Copilot &#8211; zesp\u00f3\u0142 uczy si\u0119 pracy z modelami.<\/li>\n<li><strong>Gdy brakuje dost\u0119pu do archiwum:<\/strong> <strong>RAG<\/strong> (cz\u0119sto najwi\u0119kszy skok warto\u015bci w firmach dokumentowych).<\/li>\n<li><strong>Opcjonalnie p\u00f3\u017aniej:<\/strong> agenci &#8211; automatyzacja proces\u00f3w.<\/li>\n<\/ol>\n<p>Nie musisz startowa\u0107 od najdro\u017cszego wariantu. <strong>Start with the problem:<\/strong> je\u015bli g\u0142\u00f3wny b\u00f3l to \u201eszukanie w setkach plik\u00f3w\u201d, przewodnik po narz\u0119dziach <a href=\"https:\/\/jsoncrew.com\/?p=1402\">in the first article of the series<\/a> i architektura RAG s\u0105 wa\u017cniejsze ni\u017c fine-tuning od pierwszego dnia.<\/p>\n<p>Gdy klient ko\u0144cowy ma <strong>repetitive questions<\/strong> zanim trafi do handlowca, podobn\u0105 logik\u0119 \u201eodpowiedzi z g\u00f3ry\u201d realizuje dobrze zaprojektowane <a href=\"https:\/\/jsoncrew.com\/oferta\/cyfrowe-ofertowanie-i-kalkulacje\/\">digital quoting and calculations<\/a> – a different layer, same direction: <strong>mniej chaosu, wi\u0119cej jasnych odpowiedzi<\/strong>.<\/p>\n<hr>\n<p><em>Chcesz dopasowa\u0107 architektur\u0119 do skali dokument\u00f3w i wymaga\u0144 zgodno\u015b\u0107? Napisz &#8211; przejdziemy przez Tw\u00f3j przypadek bez technicznego \u017cargonu.<\/em><\/p>\n\n\n<div class=\"kk-star-ratings kksr-auto kksr-align-left kksr-valign-bottom\"\n    data-payload='{&quot;align&quot;:&quot;left&quot;,&quot;id&quot;:&quot;1407&quot;,&quot;slug&quot;:&quot;default&quot;,&quot;valign&quot;:&quot;bottom&quot;,&quot;ignore&quot;:&quot;&quot;,&quot;reference&quot;:&quot;auto&quot;,&quot;class&quot;:&quot;&quot;,&quot;count&quot;:&quot;1&quot;,&quot;legendonly&quot;:&quot;&quot;,&quot;readonly&quot;:&quot;&quot;,&quot;score&quot;:&quot;5&quot;,&quot;starsonly&quot;:&quot;&quot;,&quot;best&quot;:&quot;5&quot;,&quot;gap&quot;:&quot;5&quot;,&quot;greet&quot;:&quot;Oce\u0144 ten artyku\u0142&quot;,&quot;legend&quot;:&quot;5 na 5 (1 g\u0142os\u00f3w)&quot;,&quot;size&quot;:&quot;24&quot;,&quot;title&quot;:&quot;Architektury AI w firmie - od czatu do RAG i agent\u00f3w&quot;,&quot;width&quot;:&quot;142.5&quot;,&quot;_legend&quot;:&quot;{score} na {best} ({count} g\u0142os\u00f3w)&quot;,&quot;font_factor&quot;:&quot;1.25&quot;}'>\n            \n<div class=\"kksr-stars\">\n    \n<div class=\"kksr-stars-inactive\">\n            <div class=\"kksr-star\" data-star=\"1\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"2\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"3\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"4\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"5\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n    <\/div>\n    \n<div class=\"kksr-stars-active\" style=\"width: 142.5px;\">\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n    <\/div>\n<\/div>\n                \n\n<div class=\"kksr-legend\" style=\"font-size: 19.2px;\">\n            5 na 5 (1 g\u0142os\u00f3w)    <\/div>\n    <\/div>\n","protected":false},"excerpt":{"rendered":"<p>Before you sign off on a budget for \u201cAI implementation,\u201d it\u2019s important to know that this isn\u2019t a single product. There are five architectures\u2014ranging from simple chatbots to RAG and agents\u2014each with its own costs and limitations.<\/p>","protected":false},"author":4,"featured_media":1408,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_jc_seo_i18n":"","jc_lang":"","jc_translation_group":"","footnotes":""},"categories":[353],"tags":[],"brevo_track":[423],"class_list":["post-1407","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-dla-firm"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v23.8 (Yoast SEO v28.6) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Architektury AI w firmie: chat, RAG, agenci \u2014 por\u00f3wnanie<\/title>\n<meta name=\"description\" content=\"Pi\u0119\u0107 architektur AI prostym j\u0119zykiem: prosty chat, pliki, RAG, agenci, fine-tuning. 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