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artificiale",":type":"snowflake-site/components/structure/page","appliedCssClassNames":"summit-page"},{"id":"breadcrumb-6167edfa42-item-25d8c2bb7f","link":{"valid":true,"url":"/it/artificial-intelligence/generative-ai/"},"active":true,"current":true,"title":"AI generativa",":type":"snowflake-site/components/structure/page","appliedCssClassNames":"summit-page"}],":type":"snowflake-site/components/breadcrumb"}},":itemsOrder":["breadcrumb"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_939100716":{"id":"flexible-column-container-739a4f1de7","propertiesId":"hub-hero","type":"2-column-even","alignColumns":"center","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"extra-small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-f51f0bfe43",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_869640791":{"id":"text-bbb094b5b7","additionalClasses":"eyebrow-text","text":"\u003Cp style=\"padding-top: 8.0px;\"\u003EGuida di riferimento\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-02"},"title_v2":{"id":"title-v2-d359a8bdab","additionalClasses":"hub-hero__headline","type":"heading1","lines":["AI generativa: come funziona e perché dati, contesto e controllo sono decisivi"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-58a48d053b","additionalClasses":"hub-hero__subheadline","text":"\u003Cp\u003EL’AI generativa sta trasformando il modo in cui le organizzazioni creano, analizzano e utilizzano le informazioni. Scopri come funziona questa tecnologia, quale ruolo hanno i dati aziendali e cosa serve per adottare la gen AI in modo responsabile.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"container":{"additionalClasses":"hub-hero__authors","layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"content_chip_copy":"aem-GridColumn aem-GridColumn--default--12","content_chip":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-7abc85cae4",":type":"snowflake-site/components/container",":items":{"content_chip":{"id":"content-chip-08558c13ba","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/blog/authors/Laurie-Macpherson/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Leggi la 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bio"},"image":{"id":"image","height":"512","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--fd9454ea-3b59-4d19-95be-534774cbd226/david.jpg?quality=85&preferwebp=true","lazyEnabled":true,"alt":"David Gaule","isLcpImage":false,"width":"512",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["David Gaule","Technical Editor, 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border-top","layout":"SIMPLE","id":"container-3e9f016f31",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_894059747":{"id":"text-5e8e60ef55","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003EApprofondimenti sull’AI generativa:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-b8580f630d","additionalClasses":"related-topics ","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/generative-ai/diffusion-model/\"\u003EModelli di diffusione\u003C/a\u003E\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_663228916":{"id":"flexible-column-container-edb02b9bc5","propertiesId":"hub-body","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"longform-content","layout":"SIMPLE","id":"hub-body-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"callout__0":{"id":"text-773f3d86c0","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EAI GENERATIVA: LA DEFINIZIONE\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EL’AI generativa è una forma di intelligenza artificiale che trasforma prompt e informazioni contestuali in nuovi contenuti, dalle risposte in linguaggio naturale al codice software fino ai contenuti multimediali.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-b941d7bea8","text":"\u003Cp\u003EL’AI generativa ha raggiunto un tasso di adozione da parte della popolazione del 53% in tre anni, secondo l’\u003Ca href=\"https://hai.stanford.edu/ai-index/2026-ai-index-report\" target=\"_blank\"\u003EAI Index 2026 di Stanford\u003C/a\u003E: una diffusione più rapida del personal computer o di internet. In questo breve arco di tempo, i modelli che avevano inizialmente attirato l’attenzione per la scrittura di testi e la generazione di immagini sono entrati a far parte dello sviluppo software, del supporto clienti, della ricerca, dell’analisi dei dati e di centinaia di altri workflow aziendali.\u003C/p\u003E\n\u003Cp\u003EI primi dibattiti sulla gen AI si concentravano su ciò che questa tecnologia poteva fare. Oggi le aziende si interrogano con la stessa attenzione su dove un modello attinge le proprie informazioni, come accede ai dati aziendali, se le sue risposte sono affidabili e quale governance è necessaria prima che gli output influenzino clienti, dipendenti o strategie di business.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-generative-ai":{"id":"title-v2-692f8cbdc1","additionalClasses":"anchor-title anchor-title--what-is-generative-ai","type":"heading2","lines":["Che cos’è l’AI generativa?"],":type":"snowflake-site/components/title-v2"},"text_what-is-generative-ai_0":{"id":"text-218e511667","text":"\u003Cp\u003EL’AI generativa è una categoria dell’\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/\"\u003Eintelligenza artificiale\u003C/a\u003E che crea nuovi output a partire dai pattern appresi durante l’addestramento. Questi output possono includere linguaggio naturale, codice software, immagini, audio, video o dati strutturati, a seconda del tipo di modello.\u003C/p\u003E\n\u003Cp\u003EInvece di risolvere un singolo problema circoscritto, un sistema di gen AI supporta un’ampia gamma di attività linguistiche e di ragionamento adattabili ad applicazioni diverse: spiegare un report finanziario, riassumere un caso di assistenza clienti, generare SQL a partire da una domanda di business, redigere documentazione e ragionare su informazioni provenienti da più fonti.\u003C/p\u003E\n\u003Cp\u003EQuesta flessibilità spiega perché l’AI generativa è diventata la base di chatbot, assistenti di programmazione, ricerca aziendale, analisi documentale, \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/agents/\"\u003Eagenti AI\u003C/a\u003E e innumerevoli altre applicazioni. Anche se agli utenti finali questi sistemi appaiono molto diversi tra loro, molti si basano sulla stessa capacità di fondo: generare nuovi contenuti in risposta a un’istruzione e al contesto fornito insieme a essa.\u003C/p\u003E\n\u003Ch3\u003EI foundation model offrono capacità ad ampio spettro\u003C/h3\u003E\n\u003Cp\u003ELa maggior parte delle applicazioni moderne di AI generativa parte da un foundation model, un modello di grandi dimensioni addestrato su data set diversificati e in grado di svolgere molte attività anziché una singola funzione specializzata.\u003C/p\u003E\n\u003Cp\u003EI \u003Ca href=\"https://www.snowflake.com/it/fundamentals/large-language-model/\"\u003Elarge language model\u003C/a\u003E (LLM) sono l’esempio più noto. Modelli come GPT, Claude, Llama e Mistral apprendono relazioni statistiche all’interno di enormi raccolte di testo e possono così generare linguaggio coerente e generalizzare ad attività per le quali non sono mai stati addestrati in modo esplicito. Altri foundation model sono specializzati in immagini, audio o video, mentre i modelli multimodali, sempre più diffusi, comprendono e generano diversi tipi di contenuto all’interno dello stesso sistema.\u003C/p\u003E\n\u003Cp\u003ELe capacità generali, tuttavia, sono diverse dalla conoscenza del business. Un foundation model non conosce intrinsecamente il catalogo prodotti aggiornato di un’organizzazione, i record dei clienti, le policy di sicurezza o i risultati finanziari. Questi dettagli risiedono all’esterno del modello e devono essere forniti nel momento in cui l’applicazione lo invoca.\u003C/p\u003E\n\u003Cp\u003E\u003Cem\u003EGuarda Baris Gultekin, Head of AI, e Vivek Raghunathan, SVP of Engineering di Snowflake, discutere degli aspetti chiave per le aziende che portano l’AI in produzione:\u003C/em\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_what-is-generative-ai_0":{"id":"embed-fd68124f3d","youtubeVideoId":"cJGebAnHbB4","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_generative-ai-vs-other-types-of-ai":{"id":"title-v2-41a7714a36","additionalClasses":"anchor-title anchor-title--generative-ai-vs-other-types-of-ai","type":"heading2","lines":["AI generativa e altri tipi di AI"],":type":"snowflake-site/components/title-v2"},"text_generative-ai-vs-other-types-of-ai_0":{"id":"text-89c2a32f26","text":"\u003Cp\u003EL’AI generativa è una branca dell’intelligenza artificiale, ma le organizzazioni continuano a utilizzare tipologie precedenti di AI e \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/machine-learning/\"\u003Emachine learning\u003C/a\u003E (ML). Le applicazioni aziendali più efficaci combinano più tecniche, scegliendo l’approccio più adatto a ciascuna fase del workflow.\u003C/p\u003E\n\u003Ch3\u003EAI generativa vs. AI predittiva\u003C/h3\u003E\n\u003Cp\u003EL’AI predittiva stima esiti futuri o sconosciuti a partire dai dati storici. I retailer, ad esempio, prevedono la domanda, le aziende manifatturiere anticipano i guasti dei macchinari e gli istituti finanziari stimano la probabilità di frodi o di insolvenza sui prestiti. L’output è in genere una probabilità, una previsione o un punteggio numerico a supporto di una decisione di business.\u003C/p\u003E\n\u003Cp\u003EL’AI generativa crea nuovi contenuti in risposta a una richiesta. Tali contenuti possono spiegare una previsione, riassumere le evidenze a supporto o generare codice software, ma la finalità di fondo è la generazione, non la previsione.\u003C/p\u003E\n\u003Ch3\u003EAI generativa vs. AI analitica\u003C/h3\u003E\n\u003Cp\u003EL’AI analitica si concentra sull’individuazione di pattern all’interno delle informazioni esistenti. Raggruppa record simili, individua anomalie, rileva tendenze e aiuta gli analisti a comprendere ciò che è già accaduto. Le piattaforme di business intelligence, i motori di raccomandazione e molti workflow tradizionali di machine learning appartengono a questa categoria.\u003C/p\u003E\n\u003Cp\u003EL’AI generativa si basa su tali insight per produrre nuovi output. Se ad esempio un sistema analitico rileva una variazione anomala nel fatturato trimestrale, un modello generativo riassume i dati sottostanti, risponde a domande di approfondimento e genera SQL o visualizzazioni a supporto di ulteriori analisi.\u003C/p\u003E\n\u003Ch3\u003EAI generativa vs. AI discriminativa\u003C/h3\u003E\n\u003Cp\u003EMolti modelli di machine learning sono discriminativi: apprendono cioè a distinguere tra categorie o esiti esistenti. Un filtro antispam, ad esempio, classifica i messaggi come spam o legittimi, mentre un modello di computer vision determina se un’immagine contiene un difetto.\u003C/p\u003E\n\u003Cp\u003EI modelli generativi apprendono distribuzioni di probabilità che consentono di produrre nuovi dati o nuove sequenze, anche se i sistemi di AI moderni combinano spesso entrambi gli approcci.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_generative-ai-vs-other-types-of-ai_0":{"id":"card-v2-e737de34da","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Intercom"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/customers/all-customers/case-study/intercom/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Leggi il case study"},"image":{"id":"image","height":"351","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--5d85ec19-9789-469e-8a15-e020fc1afe75/intercom.png?quality=85&preferwebp=true","lazyEnabled":true,"alt":"intercom logo","isLcpImage":false,"width":"624",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003EIntercom utilizza l’AI Data Cloud Snowflake, Cortex AI e Snowflake Container Services per alimentare Sales Cockpit, un’app unificata basata sull’AI che consente ai team di vendita di comprendere rapidamente il comportamento dei clienti e le performance del servizio. Realizzato in poche settimane e distribuito in pochi giorni, Sales Cockpit genera presentazioni di insight sui clienti in meno di 10 minuti anziché in 3-4 ore, riduce la preparazione delle email outbound personalizzate da 40 minuti a circa 30 secondi e fa risparmiare ai team di vendita oltre 2.000 ore al mese, con un beneficio stimato di 1,4 milioni di dollari. Le statistiche si basano su dati e stime interne di Intercom.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"title_how-does-generative-ai-work":{"id":"title-v2-89cdeaf0bd","additionalClasses":"anchor-title anchor-title--how-does-generative-ai-work","type":"heading2","lines":["Come funziona l’AI generativa?"],":type":"snowflake-site/components/title-v2"},"text_how-does-generative-ai-work_0":{"id":"text-e1c4bf4bb4","text":"\u003Cp\u003EOgni risposta prodotta da un modello di AI generativa è separata dal suo addestramento da giorni, mesi o addirittura anni. Durante l’addestramento, il modello apprende relazioni statistiche da enormi raccolte di testi, immagini, audio o altri dati. Durante l’inferenza (il momento in cui una persona invia un prompt o un’applicazione richiama il modello) applica quelle relazioni apprese per generare una nuova risposta. Distinguere queste due fasi è fondamentale per capire come funzionano i moderni sistemi di AI.\u003C/p\u003E\r\n\u003Ch3\u003EL’addestramento insegna pattern, non risposte\u003C/h3\u003E\r\n\u003Cp\u003EL’addestramento viene spesso descritto come il processo con cui si insegna a un modello a “comprendere” il linguaggio o le immagini, ma è più corretto interpretarlo come apprendimento statistico su scala enorme. Durante l’addestramento il modello elabora miliardi o addirittura migliaia di miliardi di esempi e regola progressivamente i propri parametri interni per prevedere meglio le parole mancanti, ricostruire immagini o soddisfare altri obiettivi di apprendimento. Ogni passaggio sui dati migliora leggermente la sua capacità di riconoscere le relazioni tra concetti, grammatica, struttura e significato.\u003C/p\u003E\r\n\u003Cp\u003EUn LLM, ad esempio, viene addestrato in genere prevedendo i token mancanti o successivi all’interno di un testo. Con il tempo apprende che alcune parole compaiono spesso insieme, che la sintassi segue schemi riconoscibili e che i concetti condividono relazioni semantiche. Di norma non archivia i documenti come un tradizionale database consultabile. L’addestramento codifica invece relazioni statistiche che possono poi essere applicate a richieste completamente nuove.\u003C/p\u003E\r\n\u003Cp\u003ELa scala dei moderni foundation model rende questa rappresentazione estremamente generale. Poiché i dati di addestramento coprono molti argomenti, stili di scrittura e domini, un unico modello può svolgere compiti per i quali non è stato esplicitamente addestrato. Può riassumere un contratto legale, spiegare un concetto scientifico o generare codice software perché ogni attività attinge alle relazioni apprese durante l’addestramento, e non a un algoritmo dedicato a quella specifica funzione.\u003C/p\u003E\r\n\u003Ch3\u003EL’inferenza genera la risposta un token alla volta\u003C/h3\u003E\r\n\u003Cp\u003EL’inferenza inizia quando una persona invia un prompt o un’applicazione trasmette una richiesta al modello. Nel caso di un LLM, tutto ciò che il modello riceve (prompt, documenti recuperati, cronologia della conversazione e istruzioni di sistema) viene convertito in token, piccole unità che rappresentano porzioni di testo. Il modello valuta questi token nel loro insieme, calcola la probabilità di ogni possibile token successivo nel proprio vocabolario e ne seleziona uno in base alla strategia di decodifica adottata. Il token appena generato entra a far parte dell’input per la previsione successiva e il processo si ripete fino al completamento della risposta.\u003C/p\u003E\r\n\u003Cp\u003EAnche se il processo avviene in millisecondi, la risposta non è generata tutta in una volta. Ogni token dipende da quelli che lo precedono, consentendo al modello di costruire frasi, paragrafi e interi documenti in modo incrementale. Il risultato appare spesso conversazionale perché il modello estende continuamente la sequenza in base al contesto disponibile in quel momento.\u003C/p\u003E\r\n\u003Cp\u003EQuesto processo probabilistico spiega anche perché prompt identici non producono sempre output identici. Piccole differenze nel prompt, nel contesto di supporto o nelle impostazioni del modello possono influenzare la risposta. \u003Ca href=\"https://codesignal.com/learn/courses/behavioral-benchmarking-of-llms/lessons/exploring-temperature-sensitivity-in-llm-outputs\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ELa temperatura\u003C/a\u003E modifica la forma della distribuzione di probabilità dei token del modello: valori più bassi producono in genere risposte più coerenti, mentre valori più alti consentono una gamma più ampia di output possibili.\u003C/p\u003E\r\n\u003Ch3\u003EIl contesto plasma ogni chiamata al modello\u003C/h3\u003E\r\n\u003Cp\u003EUn’idea diffusa ma errata sull’AI generativa è che sia il modello a determinare in primo luogo la qualità della risposta. In pratica, le informazioni fornite durante l’inferenza hanno spesso un peso altrettanto rilevante. “Su scala enterprise, il problema più difficile non è più la sola intelligenza del modello: è il contesto”, \u003Ca href=\"https://www.snowflake.com/it/blog/snowflake-for-ai-enterprise-ai-platform/\"\u003Eafferma Baris Gultekin\u003C/a\u003E, Vice President of Product, AI, Snowflake. Un foundation model performante offre ampie capacità di ragionamento e generazione, ma è l’applicazione che lo circonda a determinare quali informazioni aziendali, policy e strumenti sono disponibili per ciascuna richiesta.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_how-does-generative-ai-work_0":{"id":"quote-item-06caf2e0c6","alignment":"left","layout":"simple","quoteSourceName":"Baris Gultekin","quoteSourceTitle":"VP of Product, AI Snowflake","showQuoteIcon":true,"quote":"Su scala aziendale, il problema più difficile non è più la sola intelligenza del modello: è il contesto.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"text_how-does-generative-ai-work_1":{"id":"text-62ee722544","text":"\u003Cp\u003EOgni modello ha una finestra di contesto, ovvero la quantità di informazioni che può prendere in considerazione in una singola richiesta. Questo contesto può includere il prompt dell’utente, la cronologia della conversazione, i documenti aziendali recuperati, i dati di business strutturati, gli output degli strumenti e le istruzioni di sistema che guidano il comportamento del modello. Nel loro insieme, questi input definiscono ciò che il modello sa per quella specifica interazione.\u003C/p\u003E\n\u003Cp\u003EFornire più informazioni, però, non migliora necessariamente il risultato. Una finestra di contesto ampia dà all’applicazione più spazio per fornire evidenze pertinenti, ma aumenta anche la probabilità che dettagli importanti si perdano tra informazioni non correlate. Documenti accurati ma non pertinenti possono distogliere il modello dall’attività in corso, mentre informazioni obsolete o contraddittorie moltiplicano le occasioni di ragionamento errato. I sistemi di AI efficaci non si concentrano quindi soltanto sull’ampliamento delle finestre di contesto, ma sulla selezione, organizzazione e definizione delle priorità delle informazioni che entrano in ogni chiamata al modello.\u003C/p\u003E\n\u003Cp\u003EQuesta sfida è diventata uno dei principali problemi ingegneristici dell’AI in ambito enterprise. Anziché inviare al modello ogni documento disponibile, le applicazioni recuperano sempre più spesso solo le informazioni più pertinenti alla richiesta, le organizzano in un contesto utile e applicano i controlli di sicurezza prima che il modello inizi a generare una risposta. Queste tecniche sono diventate centrali nei sistemi di AI in produzione e spiegano perché le applicazioni enterprise dipendono da molto più del solo foundation model.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_how-does-generative-ai-work_0":{"id":"text-c8f681216f","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003ECONSIGLIO RAPIDO\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EPiù contesto non significa sempre risultati migliori. Dai priorità alle informazioni più pertinenti, aggiornate e autorevoli, invece di riempire la finestra di contesto con ogni documento disponibile.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_what-can-generative-ai-create":{"id":"title-v2-c27a682064","additionalClasses":"anchor-title anchor-title--what-can-generative-ai-create","type":"heading2","lines":["Che cosa può creare l’AI generativa?"],":type":"snowflake-site/components/title-v2"},"text_what-can-generative-ai-create_0":{"id":"text-79b1658307","text":"\u003Cp\u003EL’AI generativa è spesso associata a chatbot e generatori di immagini, ma sono solo due esempi di un insieme di capacità molto più ampio. I moderni foundation model generano molti tipi diversi di contenuti e, sempre più spesso, un singolo modello multimodale opera su più formati all’interno della stessa applicazione.\u003C/p\u003E\n\u003Ch3\u003ETesto e linguaggio naturale\u003C/h3\u003E\n\u003Cp\u003ELa generazione di testo resta l’applicazione più diffusa dell’AI generativa. Gli LLM possono redigere email, riassumere report lunghi, rispondere a domande, tradurre documenti, spiegare concetti tecnici e generare risposte conversazionali in linguaggio naturale.\u003C/p\u003E\n\u003Cp\u003ENelle aziende, queste capacità supportano sempre più la gestione della conoscenza, l’analisi dei documenti, il customer support, la ricerca, la business intelligence e le applicazioni di produttività.\u003C/p\u003E\n\u003Ch3\u003ECodice software\u003C/h3\u003E\n\u003Cp\u003EL’AI generativa è diventata uno strumento importante per lo sviluppo software. Gli assistenti di coding possono generare codice boilerplate, spiegare funzioni non familiari, suggerire opportunità di refactoring, creare unit test e tradurre codice tra linguaggi di programmazione diversi. I \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/agents/coding-agents/\"\u003Ecoding agent\u003C/a\u003E ragionano su interi repository, aiutando gli sviluppatori a comprendere le dipendenze, individuare i file rilevanti e proporre modifiche coordinate su più componenti.\u003C/p\u003E\n\u003Cp\u003EQuesti sistemi non eliminano la necessità di competenze di ingegneria del software. Gli sviluppatori continuano a rivedere il codice generato, verificarne la correttezza e valutare le decisioni architetturali. L’AI generativa riduce invece il tempo dedicato alle attività di implementazione ripetitive, aiutando gli ingegneri a lavorare in modo più efficace su codebase complesse.\u003C/p\u003E\n\u003Ch3\u003EImmagini, audio e video\u003C/h3\u003E\n\u003Cp\u003EI modelli di generazione di immagini creano illustrazioni, concept di prodotto, asset di marketing e immagini fotorealistiche a partire da descrizioni in linguaggio naturale. Tecniche simili supportano l’editing delle immagini, la sostituzione dello sfondo, l’inpainting e il trasferimento di stile, consentendo di modificare contenuti esistenti oltre che di generarli da zero.\u003C/p\u003E\n\u003Cp\u003EL’AI generativa si è estesa anche all’audio e al video. I modelli moderni sintetizzano il parlato, generano musica, clonano voci, creano effetti sonori e producono video sempre più realistici a partire da testo, immagini o brevi clip. Se da un lato queste capacità continuano a migliorare rapidamente, dall’altro sollevano questioni importanti su autenticità, proprietà intellettuale e uso responsabile.\u003C/p\u003E\n\u003Ch3\u003EDati strutturati e sintetici\u003C/h3\u003E\n\u003Cp\u003ENon tutti i modelli generativi producono contenuti leggibili dalle persone. Alcuni generano output strutturati come query SQL, documenti JSON, richieste API o definizioni di workflow che i sistemi software possono eseguire direttamente. Altri creano dati sintetici progettati per assomigliare a set di dati reali. Se generati, valutati e protetti dalla memorizzazione in modo adeguato, i dati sintetici possono supportare i test, lo sviluppo dei modelli e la ricerca in ambiti sensibili per la privacy.\u003C/p\u003E\n\u003Cp\u003ELa generazione strutturata è diventata particolarmente importante man mano che le applicazioni AI interagiscono con database, API e sistemi aziendali. Invece di limitarsi a rispondere a domande, i modelli possono generare le informazioni strutturate necessarie per recuperare dati, invocare strumenti o automatizzare parti di un processo aziendale.\u003C/p\u003E\n\u003Ch3\u003EOutput multimodali\u003C/h3\u003E\n\u003Cp\u003EI confini tra questi tipi di output stanno progressivamente scomparendo. Un modello multimodale può analizzare un grafico, spiegare i risultati in linguaggio naturale, generare codice Python per ricreare la visualizzazione e produrre una presentazione che sintetizza le conclusioni, tutto nell’ambito della stessa conversazione.\u003C/p\u003E\n\u003Cp\u003EQuesta convergenza sta cambiando il modo in cui le organizzazioni concepiscono le applicazioni AI. Invece di implementare sistemi separati per linguaggio, visione e audio, le aziende costruiscono sempre più spesso workflow basati su modelli che ragionano su più forme di informazione. Con l’ampliarsi di queste capacità, la qualità del contesto aziendale sottostante diventa ancora più importante.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_data-for-generative-ai":{"id":"title-v2-02be991a13","additionalClasses":"anchor-title anchor-title--data-for-generative-ai","type":"heading2","lines":["Dati per l’AI generativa"],":type":"snowflake-site/components/title-v2"},"text_data-for-generative-ai_0":{"id":"text-baa5dd8afe","text":"\u003Cp\u003EL’addestramento conferisce a un foundation model capacità generali, ma non gli fornisce una conoscenza aggiornata del business di un’organizzazione. Anagrafiche dei clienti, livelli di magazzino, contratti, documentazione di prodotto, \u003Ca href=\"https://www.snowflake.com/it/data-governance/policy/\"\u003Epolicy di governance\u003C/a\u003E e dati finanziari cambiano continuamente. Anche se parte di queste informazioni era presente nei dati di addestramento di un modello, oggi potrebbe essere obsoleta, incompleta o inaccessibile a causa di requisiti di sicurezza e privacy. Normative come il GDPR e le linee guida del Garante per la protezione dei dati personali pongono vincoli specifici sull'utilizzo dei dati nei sistemi di AI.\u003C/p\u003E\n\u003Cp\u003EPrima che un modello generi una risposta, l’applicazione individua in genere le informazioni pertinenti alla richiesta, verifica che l’utente sia autorizzato ad accedervi e le assembla nel contesto del modello. La qualità della risposta dipende non solo dalle capacità di ragionamento del modello, ma anche dalla qualità, dalla pertinenza e dalla governance delle informazioni fornite in fase di inferenza.\u003C/p\u003E\n\u003Ch3\u003EI dati di addestramento definiscono le capacità generali\u003C/h3\u003E\n\u003Cp\u003EOgni modello di AI generativa parte dai dati di addestramento. I foundation model apprendono relazioni statistiche da vaste raccolte di contenuti pubblicamente disponibili, concessi in licenza e, in alcuni casi, creati da persone o sintetici. La composizione di questi dati influenza tutto: dalla fluidità linguistica e dalla capacità di scrivere codice fino alle prestazioni multilingue e alla conoscenza di dominio.\u003C/p\u003E\n\u003Cp\u003EI dati di addestramento, tuttavia, hanno una funzione radicalmente diversa da quella dei dati aziendali. Il loro ruolo è insegnare schemi generali che permettono a un modello di generare output coerenti in molti compiti diversi. Una volta completato l’addestramento, la conoscenza del modello non si aggiorna automaticamente quando vengono redatti nuovi documenti o le informazioni aziendali evolvono. Aggiornare quella conoscenza richiede in genere ulteriore addestramento o fine-tuning, operazioni costose dal punto di vista computazionale e impraticabili per informazioni che cambiano frequentemente.\u003C/p\u003E\n\u003Ch3\u003EI dati aziendali forniscono il contesto di business\u003C/h3\u003E\n\u003Cp\u003EUn cliente che chiede informazioni su un ordine, un analista che esamina il fatturato trimestrale e un tecnico che risolve un problema applicativo hanno tutti bisogno di informazioni che esistono al di fuori del foundation model. Le applicazioni di AI aziendale recuperano queste informazioni in fase di inferenza, così il modello può ragionare su dati aggiornati e autorevoli invece di basarsi solo su ciò che ha appreso durante l’addestramento.\u003C/p\u003E\n\u003Cp\u003EIl retrieval è diventato uno dei pattern architetturali distintivi dell’AI aziendale perché separa il ragionamento generale dalla conoscenza di business. Invece di riaddestrare continuamente un modello al variare dei dati, le applicazioni interrogano database operativi, data warehouse, repository documentali e knowledge base immediatamente prima di invocare il modello. Le informazioni recuperate diventano parte del contesto di quella chiamata al modello, permettendo alle risposte di riflettere le informazioni aziendali aggiornate senza modificare il modello sottostante.\u003C/p\u003E\n\u003Cp\u003EQuesta architettura favorisce anche una governance più solida. Poiché i dati restano nei sistemi aziendali invece di essere incorporati in modo permanente nei pesi del modello, le organizzazioni mantengono un maggiore controllo su permessi, lineage, criteri di conservazione e audit. Gli aggiornamenti a un documento di policy o a un catalogo prodotti diventano disponibili appena il livello di retrieval accede alla versione più recente, senza attendere il riaddestramento del modello.\u003C/p\u003E\n\u003Ch3\u003EEmbedding e ricerca vettoriale per recuperare le informazioni pertinenti\u003C/h3\u003E\n\u003Cp\u003ETrovare le informazioni giuste è spesso più difficile che accedervi. La ricerca tradizionale per parole chiave funziona bene quando si sa esattamente quali parole compaiono in un documento, ma le domande in linguaggio naturale raramente corrispondono con precisione al testo archiviato. Chi chiede “Perché il fatturato è calato in Europa?” potrebbe non usare la stessa terminologia presente in un report trimestrale o in una dashboard di vendita.\u003C/p\u003E\n\u003Cp\u003ELe moderne applicazioni AI si affidano sempre più agli embedding per colmare questo divario. Un embedding è una rappresentazione numerica dei dati che ne cattura il significato semantico anziché la formulazione esatta. Un testo che parla di “customer churn”, ad esempio, può collocarsi vicino a contenuti sulle “disdette degli abbonamenti” anche se le due espressioni hanno poche parole in comune. Le idee simili occupano posizioni vicine all’interno di uno spazio vettoriale a molte dimensioni, il che ne facilita il recupero in base al significato anziché al testo letterale.\u003C/p\u003E\n\u003Cp\u003EQuesti vettori sono in genere archiviati in un indice vettoriale o in un database vettoriale, consentendo alle applicazioni di individuare i documenti semanticamente correlati alla richiesta dell’utente. Invece di cercare parole chiave corrispondenti, l’applicazione cerca vettori vicini che rappresentano concetti simili. I passaggi recuperati sono poi forniti al modello come contesto aggiuntivo prima dell’avvio della generazione.\u003C/p\u003E\n\u003Cp\u003EGli embedding migliorano la capacità dell’applicazione di selezionare il contesto pertinente. Per questo sono diventati un componente fondamentale della \u003Ca href=\"https://www.snowflake.com/it/fundamentals/rag/\"\u003Eretrieval-augmented generation\u003C/a\u003E (RAG), della ricerca semantica e di molti agenti AI che ragionano sulla conoscenza aziendale.\u003C/p\u003E\n\u003Ch3\u003ELa context engineering determina ciò che il modello conosce durante l’inferenza\u003C/h3\u003E\n\u003Cp\u003ELe informazioni recuperate dai sistemi aziendali diventano parte del contesto del modello, ossia l’insieme degli input disponibili durante una singola richiesta di inferenza. Questo contesto può includere istruzioni dell’utente, documenti recuperati, dati aziendali strutturati, la cronologia delle conversazioni precedenti, output degli strumenti e prompt di sistema che guidano il comportamento del modello.\u003C/p\u003E\n\u003Cp\u003ELa context engineering è diventata una disciplina a sé. Le applicazioni devono decidere quali informazioni sono pertinenti, risolvere fonti duplicate o contrastanti, conservare i dettagli importanti rimanendo entro i limiti della finestra di contesto e garantire che le informazioni sensibili siano condivise solo con utenti autorizzati. Man mano che le applicazioni di AI generativa diventano più sofisticate, gran parte del lavoro di engineering si sposta verso l’assemblaggio di un contesto accurato, pertinente e governato per ogni richiesta.\u003C/p\u003E\n\u003Cp\u003EIl risultato è che due applicazioni che utilizzano lo stesso foundation model possono produrre esiti radicalmente diversi. La differenza spesso non sta nel modello, ma nei dati che ciascuna applicazione recupera, nel contesto che assembla e nei controlli che regolano il modo in cui queste informazioni raggiungono il modello.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-are-the-risks-of-generative-ai":{"id":"title-v2-bfba9605cd","additionalClasses":"anchor-title anchor-title--what-are-the-risks-of-generative-ai","type":"heading2","lines":["Quali sono i rischi dell’AI generativa?"],":type":"snowflake-site/components/title-v2"},"text_what-are-the-risks-of-generative-ai_0":{"id":"text-cd1c0c471b","text":"\u003Cp\u003EOgni applicazione di AI generativa compie una serie di previsioni: quale token deve seguire, quale documento è più pertinente da recuperare, quale strumento invocare e, nei sistemi più avanzati, quali azioni eseguire per completare un compito. Queste previsioni rendono l’AI generativa straordinariamente flessibile, ma introducono anche un’incertezza che il software tradizionale non deve gestire.\u003C/p\u003E\r\n\u003Cp\u003EIl report Snowflake \u003Ca href=\"https://www.snowflake.com/it/lp/radical-roi-generative-ai/\"\u003EIl ROI dell’AI generativa e degli agenti AI nel 2026\u003C/a\u003E rivela che il 40% degli intervistati indica la qualità e la quantità dei dati come una sfida importante, mentre il 65% dichiara di avere difficoltà ad abbattere i silos di dati per l’AI. Il 56% ha segnalato almeno una sfida di governance o compliance.\u003C/p\u003E\r\n\u003Ch3\u003ELe allucinazioni si verificano quando i modelli generano informazioni plausibili ma prive di fondamento\u003C/h3\u003E\r\n\u003Cp\u003EI modelli di AI generativa sono ottimizzati per produrre risposte coerenti e appropriate al contesto. Non sono in grado di stabilire se ogni affermazione che generano sia corretta dal punto di vista dei fatti. Quando un modello non dispone di prove sufficienti o interpreta male il contesto disponibile, può produrre informazioni che sembrano affidabili e ben scritte ma che sono infondate o errate. Questi output inventati o inesatti sono comunemente chiamati allucinazioni.\u003C/p\u003E\r\n\u003Cp\u003ELe allucinazioni possono avere diverse cause. Un modello può avere una conoscenza incompleta perché le informazioni pertinenti non erano incluse nei suoi dati di addestramento. Oppure l’applicazione può recuperare documenti obsoleti o non pertinenti, lasciando il modello a ragionare su prove incomplete. Le finestre di contesto ampie possono introdurre informazioni concorrenti o contraddittorie, mentre i prompt ambigui possono spingere il modello a colmare le lacune con un linguaggio statisticamente plausibile.\u003C/p\u003E\r\n\u003Cp\u003ENon tutte le allucinazioni hanno le stesse conseguenze. Il livello di incertezza accettabile dipende dalla decisione che il sistema supporta e dal potenziale impatto di una risposta errata.\u003C/p\u003E\r\n\u003Ch3\u003ESicurezza e privacy vanno oltre il modello\u003C/h3\u003E\r\n\u003Cp\u003EI sistemi di \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/business/enterprise/\"\u003EAI aziendale\u003C/a\u003E operano su informazioni che spesso includono anagrafiche dei clienti, dati finanziari, proprietà intellettuale e altri asset sensibili. Proteggere queste informazioni richiede più della semplice scelta di un provider di modelli sicuro. Ogni fase dell’applicazione, dal recupero dei dati alla generazione della risposta fino alla memorizzazione della cronologia delle conversazioni, deve applicare le policy di sicurezza e di accesso già in vigore nell’organizzazione.\u003C/p\u003E\r\n\u003Cp\u003EQuesto requisito diventa particolarmente importante quando le applicazioni AI acquisiscono la capacità di utilizzare strumenti esterni o di eseguire azioni per conto di un utente. Un modello in grado di interrogare un database, aggiornare il record di un cliente o eseguire codice eredita le autorizzazioni concesse dall’applicazione che lo integra. Integrazioni progettate male possono esporre informazioni sensibili o consentire al modello di eseguire azioni che eccedono le autorizzazioni dell’utente richiedente.\u003C/p\u003E\r\n\u003Cp\u003EUna sicurezza efficace dell’AI si fonda su principi di sicurezza già noti. Identità, autenticazione, autorizzazione, crittografia, monitoraggio e audit restano controlli essenziali. La differenza è che questi controlli si estendono ora alle chiamate ai modelli, ai sistemi di retrieval e ai workflow AI, oltre che alle applicazioni tradizionali.\u003C/p\u003E\r\n\u003Ch3\u003EBias e proprietà intellettuale richiedono una supervisione continua\u003C/h3\u003E\r\n\u003Cp\u003EL’AI generativa riflette i pattern presenti nei dati usati per l’addestramento e nelle informazioni fornite durante l’inferenza. Se queste fonti contengono bias storici, rappresentazioni incomplete o punti di vista contrastanti, gli output del modello possono rafforzare quegli stessi pattern. Analogamente, i contenuti generati possono somigliare a materiale protetto da copyright o, in determinate circostanze, riprodurre informazioni sensibili, con implicazioni legali ed etiche che le organizzazioni devono valutare prima di adottare l’AI su larga scala.\u003C/p\u003E\r\n\u003Cp\u003EQuesti aspetti hanno raramente una soluzione puramente tecnica. Le organizzazioni adottano sempre più processi di \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/\"\u003Egovernance dell’AI\u003C/a\u003E che definiscono i casi d’uso ammessi, valutano equità e affidabilità dei modelli, documentano lo sviluppo dei sistemi AI e ne monitorano il comportamento dopo il rilascio. La supervisione umana resta particolarmente importante quando l’AI supporta decisioni che riguardano clienti, dipendenti, sanità, finanza o altre attività regolamentate. In Europa, l'\u003Ca target=\"_blank\" href=\"https://eur-lex.europa.eu/eli/reg/2024/1689/oj?locale=it\" rel=\"noopener noreferrer\"\u003EAI Act (Reg. UE 2024/1689)\u003C/a\u003E, applicabile dal 2 agosto 2026, classifica questi contesti come sistemi ad alto rischio e impone requisiti specifici di supervisione umana; in Italia, il \u003Ca href=\"https://author-p57963-e462109.adobeaemcloud.com/ui#/aem/editor.html/content/snowflake-site/global/it/artificial-intelligence/generative-ai.html\" target=\"_blank\"\u003EGarante per la protezione dei dati personali\u003C/a\u003E ha emanato indicazioni specifiche sull'uso dell'AI nei settori regolamentati.\u003C/p\u003E\r\n\u003Ch3\u003EIl ruolo della governance\u003C/h3\u003E\r\n\u003Cp\u003ELa governance deve seguire l’interazione lungo l’intero workflow. Gli stessi controlli di accesso, policy di mascheramento, audit trail e requisiti di monitoraggio devono applicarsi sia quando un utente interroga direttamente i dati sia quando un agente AI accede a quei dati ed esegue un’azione per conto dell’utente.\u003C/p\u003E\r\n\u003Cp\u003EIl livello di governance appropriato dipende da ciò che il sistema è autorizzato a fare. Riassumere documentazione interna, suggerire un titolo per una campagna di marketing, approvare una richiesta di prestito ed eseguire una transazione finanziaria comportano livelli di rischio diversi, anche quando lo stesso foundation model contribuisce a ciascun workflow.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_evaluation-and-guardrails-help-ai-systems-perform-reliably":{"id":"title-v2-aa4bead74a","additionalClasses":"anchor-title anchor-title--evaluation-and-guardrails-help-ai-systems-perform-reliably","type":"heading2","lines":["Valutazione e guardrail rendono affidabili i sistemi AI"],":type":"snowflake-site/components/title-v2"},"text_evaluation-and-guardrails-help-ai-systems-perform-reliably_0":{"id":"text-be55f6fbff","text":"\u003Cp\u003ELa valutazione combina spesso test automatizzati e giudizio umano. 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