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style=\"padding-top: 8.0px;\"\u003EGrundlagen\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-02"},"title_v2":{"id":"title-v2-6cc9fdd6ee","additionalClasses":"hub-hero__headline","type":"heading1","lines":["Generative KI: Wie sie funktioniert und warum Daten, Kontext und Kontrolle wichtig sind"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-b410d22afa","additionalClasses":"hub-hero__subheadline","text":"\u003Cp\u003EGenerative KI verändert, wie Unternehmen Informationen erstellen, analysieren und darauf reagieren. Erfahren Sie, wie die Technologie funktioniert, wo Unternehmensdaten ins Spiel kommen und was erforderlich ist, um GenAI verantwortungsvoll einzusetzen.\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,"columnClassNames":{"content_chip_copy":"aem-GridColumn aem-GridColumn--default--12","content_chip":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-9ee380fde7",":type":"snowflake-site/components/container",":items":{"content_chip":{"id":"content-chip-8cc57456e1","cta":{"id":"cta","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Biografie 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border-top","layout":"SIMPLE","id":"container-77b6466f27",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_894059747":{"id":"text-9b5bbdbf52","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003EThemen rund um generative KI:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-d10dc659ff","additionalClasses":"related-topics ","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/generative-ai/diffusion-model/\"\u003EDiffusionsmodelle\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-cced06dfd0","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-c8039f06b4","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EGENERATIVE KI DEFINIERT\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EGenerative KI ist eine Form der künstlichen Intelligenz, die Prompts und Kontextinformationen in neu generierte Inhalte verwandelt, von Antworten in natürlicher Sprache bis hin zu Softwarecode und Multimedia.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-6708475253","text":"\u003Cp\u003ELaut dem \u003Ca href=\"https://hai.stanford.edu/ai-index/2026-ai-index-report\" target=\"_blank\"\u003EStanford AI Index 2026\u003C/a\u003E erreichte generative KI innerhalb von drei Jahren eine Akzeptanz von 53 % in der Bevölkerung – schneller als der Personal Computer oder das Internet. In dieser kurzen Zeit sind Modelle, die zunächst durch das Schreiben von Aufsätzen und das Generieren von Bildern Aufmerksamkeit erregten, zu einem Bestandteil von Softwareentwicklung, Kundensupport, Forschung, Analytics und Hunderten anderer Geschäftsabläufe geworden.\u003C/p\u003E\n\u003Cp\u003EFrühe Diskussionen über GenAI konzentrierten sich darauf, was sie leisten kann. Heute beschäftigen sich Unternehmen gleichermaßen damit, woher ein Modell seine Informationen bezieht, wie es auf Geschäftsdaten zugreift, ob seinen Antworten vertraut werden kann und welche Governance erforderlich ist, bevor die Ergebnisse Kund:innen, Mitarbeiter:innen oder die Geschäftsstrategie beeinflussen.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-generative-ai":{"id":"title-v2-2a49b669d9","additionalClasses":"anchor-title anchor-title--what-is-generative-ai","type":"heading2","lines":["Was ist generative KI?"],":type":"snowflake-site/components/title-v2"},"text_what-is-generative-ai_0":{"id":"text-37bf487297","text":"\u003Cp\u003EGenerative KI ist eine Kategorie der \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/\"\u003Ekünstlichen Intelligenz\u003C/a\u003E, die aus Mustern, die während des Trainings erlernt wurden, neue Ergebnisse erstellt. Diese Ergebnisse können je nach Modelltyp natürliche Sprache, Softwarecode, Bilder, Audio, Video oder strukturierte Daten umfassen.\u003C/p\u003E\n\u003Cp\u003EAnstatt ein eng definiertes Problem zu lösen, unterstützt ein GenAI-System eine breite Palette von Sprach- und Schlussfolgerungsaufgaben, die an verschiedene Anwendungen angepasst werden können: die Erklärung eines Finanzberichts, die Zusammenfassung eines Kundensupportfalls, die Generierung von SQL aus einer Geschäftsfrage, das Verfassen von Dokumentationen und das Ziehen von Schlüssen aus Informationen aus mehreren Quellen.\u003C/p\u003E\n\u003Cp\u003EDiese Flexibilität erklärt, warum generative KI zur Grundlage für Chatbots, Programmierassistenten, Unternehmenssuche, Dokumentenanalyse, \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/agents/\"\u003EKI-Agenten\u003C/a\u003E und unzählige andere Anwendungen geworden ist. Obwohl diese Systeme für Endnutzer:innen sehr unterschiedlich erscheinen, verlassen sich viele auf dieselbe zugrunde liegende Fähigkeit: die Generierung neuer Inhalte als Reaktion auf eine Anweisung und den damit bereitgestellten Kontext.\u003C/p\u003E\n\u003Ch3\u003EFoundation-Modelle bieten umfassende Fähigkeiten\u003C/h3\u003E\n\u003Cp\u003EDie meisten modernen Anwendungen für generative KI beginnen mit einem Foundation-Modell – einem großen Modell, das mit vielfältigen Datasets trainiert wurde und viele Aufgaben anstelle einer einzigen spezialisierten Funktion ausführen kann.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/fundamentals/large-language-model/\"\u003ELarge Language Models\u003C/a\u003E (LLMs) sind das bekannteste Beispiel. Modelle wie GPT, Claude, Llama und Mistral erlernen statistische Beziehungen über riesige Textsammlungen hinweg, was es ihnen ermöglicht, kohärente Sprache zu generieren und auf Aufgaben zu verallgemeinern, für die sie nie explizit trainiert wurden. Andere Foundation-Modelle sind auf Bilder, Audio oder Video spezialisiert, während zunehmend multimodale Modelle mehrere Arten von Inhalten innerhalb desselben Systems verstehen und generieren können.\u003C/p\u003E\n\u003Cp\u003EAllgemeine Fähigkeiten unterscheiden sich jedoch von Geschäftswissen. Ein Foundation-Modell kennt von Natur aus nicht den aktuellen Produktkatalog, die Kundendatensätze, die Sicherheitsrichtlinien oder die Finanzergebnisse eines Unternehmens. Diese Details existieren außerhalb des Modells und müssen bereitgestellt werden, wenn die Anwendung es aufruft.\u003C/p\u003E\n\u003Cp\u003E\u003Cem\u003ESehen Sie sich an, wie Baris Gultekin, Head of AI bei Snowflake, und Vivek Raghunathan, SVP of Engineering, wichtige Überlegungen für Unternehmen diskutieren, die KI in die Produktion überführen:\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-670ce17462","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-v2-4a44956448","additionalClasses":"anchor-title anchor-title--generative-ai-vs-other-types-of-ai","type":"heading2","lines":["Generative KI im Vergleich zu anderen Arten von KI"],":type":"snowflake-site/components/title-v2"},"text_generative-ai-vs-other-types-of-ai_0":{"id":"text-fc56727515","text":"\u003Cp\u003EGenerative KI ist ein Zweig der künstlichen Intelligenz, aber Unternehmen verlassen sich weiterhin auf frühere Arten von KI und \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/\"\u003Emaschinelles Lernen\u003C/a\u003E (ML). Die leistungsfähigsten Unternehmensanwendungen kombinieren mehrere Techniken und wählen den Ansatz, der am besten zu jeder Phase des Workflows passt.\u003C/p\u003E\n\u003Ch3\u003EGenerative KI im Vergleich zu prädiktiver KI\u003C/h3\u003E\n\u003Cp\u003EPrädiktive KI schätzt zukünftige oder unbekannte Ergebnisse anhand historischer Daten. Beispielsweise prognostizieren Einzelhändler die Nachfrage, Hersteller sagen Geräteausfälle voraus und Finanzinstitute schätzen die Wahrscheinlichkeit von Betrug oder Kreditausfällen. Das Ergebnis ist typischerweise eine Wahrscheinlichkeit, eine Prognose oder ein numerischer Wert, der eine Geschäftsentscheidung unterstützt.\u003C/p\u003E\n\u003Cp\u003EGenerative KI erstellt neue Inhalte als Reaktion auf eine Anfrage. Diese Inhalte können eine Prognose erklären, unterstützende Beweise zusammenfassen oder Softwarecode generieren, aber der zugrunde liegende Zweck ist die Generierung und nicht die Vorhersage.\u003C/p\u003E\n\u003Ch3\u003EGenerative KI im Vergleich zu analytischer KI\u003C/h3\u003E\n\u003Cp\u003EAnalytische KI konzentriert sich auf die Entdeckung von Mustern innerhalb vorhandener Informationen. Sie gruppiert ähnliche Datensätze, identifiziert Anomalien, erkennt Trends und hilft Analyst:innen zu verstehen, was bereits geschehen ist. Business Intelligence-Plattformen, Empfehlungsmaschinen und viele traditionelle Workflows für maschinelles Lernen fallen in diese Kategorie.\u003C/p\u003E\n\u003Cp\u003EGenerative KI baut auf diesen Erkenntnissen auf, indem sie neue Ergebnisse produziert. Nachdem ein analytisches System beispielsweise eine ungewöhnliche Änderung des Quartalsumsatzes identifiziert hat, kann ein generatives Modell die zugrunde liegenden Daten zusammenfassen, Folgefragen beantworten und SQL oder Visualisierungen generieren, um zusätzliche Analysen zu unterstützen.\u003C/p\u003E\n\u003Ch3\u003EGenerative KI im Vergleich zu diskriminativer KI\u003C/h3\u003E\n\u003Cp\u003EViele Modelle für maschinelles Lernen sind diskriminativ, was bedeutet, dass sie lernen, zwischen bestehenden Kategorien oder Ergebnissen zu unterscheiden. Ein E-Mail-Spamfilter klassifiziert Nachrichten beispielsweise als Spam oder legitim, und ein Computer-Vision-Modell erkennt, ob ein Bild einen Defekt aufweist.\u003C/p\u003E\n\u003Cp\u003EGenerative Modelle erlernen Wahrscheinlichkeitsverteilungen, die es ihnen ermöglichen, neue Daten oder Sequenzen zu produzieren, obwohl moderne KI-Systeme oft beide Ansätze kombinieren.\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-v2-74586adf6b","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":"Customer Story lesen (Englisch)"},"image":{"id":"image","height":"351","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--43c070c5-1ab2-4ac0-8a6c-094ab483907c/intercom.png?quality=85&preferwebp=true","alt":"intercom logo","lazyEnabled":true,"isLcpImage":false,"width":"624",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003EIntercom nutzt die AI Data Cloud von Snowflake, Cortex AI und Snowflake Container Services, um Sales Cockpit zu betreiben, eine einheitliche KI-gesteuerte App, die Vertriebsteams hilft, Kundenverhalten und Service-Performance schnell zu verstehen. Sales Cockpit wurde in Wochen entwickelt und in Tagen bereitgestellt. Es generiert Präsentationen mit Kunden-Erkenntnissen in weniger als 10 Minuten statt in 3–4 Stunden, reduziert die Vorbereitung personalisierter ausgehender E-Mails von 40 Minuten auf etwa 30 Sekunden und spart Vertriebsteams mehr als 2.000 Stunden pro Monat – ein geschätzter Vorteil von 1,4 Millionen US-Dollar. Die Statistiken basieren auf internen Daten und Schätzungen von Intercom.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"title_how-does-generative-ai-work":{"id":"title-v2-f2901fa346","additionalClasses":"anchor-title anchor-title--how-does-generative-ai-work","type":"heading2","lines":["Wie funktioniert generative KI?"],":type":"snowflake-site/components/title-v2"},"text_how-does-generative-ai-work_0":{"id":"text-016358cb82","text":"\u003Cp\u003EJede Antwort, die ein Modell für generative KI produziert, ist durch Tage, Monate oder sogar Jahre von seinem Training getrennt. Während des Trainings erlernt das Modell statistische Beziehungen aus riesigen Sammlungen von Text, Bildern, Audio oder anderen Daten. Während der Inferenz – dem Moment, in dem jemand einen Prompt übermittelt oder eine Anwendung das Modell aufruft – wendet es diese erlernten Beziehungen an, um eine neue Antwort zu generieren. Die Trennung dieser beiden Phasen ist grundlegend für das Verständnis, wie moderne KI-Systeme funktionieren.\u003C/p\u003E\r\n\u003Ch3\u003ETraining lehrt Muster, keine Antworten\u003C/h3\u003E\r\n\u003Cp\u003ETraining wird oft so beschrieben, als würde man einem Modell beibringen, Sprache oder Bilder zu „verstehen“, aber der Prozess ist besser als statistisches Lernen in enormem Maßstab zu verstehen. Während des Trainings verarbeitet ein Modell Milliarden oder sogar Billionen von Beispielen und passt seine internen Parameter schrittweise an, um fehlende Wörter besser vorherzusagen, Bilder zu rekonstruieren oder andere Lernziele zu erreichen. Jeder Durchlauf durch die Daten verbessert seine Fähigkeit, Beziehungen zwischen Konzepten, Grammatik, Struktur und Bedeutung zu erkennen, geringfügig.\u003C/p\u003E\r\n\u003Cp\u003EEin LLM wird beispielsweise typischerweise trainiert, indem fehlende oder nachfolgende Token innerhalb eines Textes vorhergesagt werden. Im Laufe der Zeit lernt es, dass bestimmte Wörter häufig zusammen auftreten, dass die Syntax erkennbaren Mustern folgt und dass Konzepte semantische Beziehungen teilen. Es speichert Dokumente normalerweise nicht wie eine herkömmliche durchsuchbare Datenbank. Stattdessen kodiert das Training statistische Beziehungen, die später auf völlig neue Anfragen angewendet werden können.\u003C/p\u003E\r\n\u003Cp\u003EDer Maßstab moderner Foundation-Modelle macht diese Darstellung bemerkenswert allgemein. Da Trainingsdaten viele Themen, Schreibstile und Domänen umfassen, kann ein einziges Modell Aufgaben ausführen, für deren Lösung es nie explizit trainiert wurde. Es kann einen rechtlichen Vertrag zusammenfassen, ein wissenschaftliches Konzept erklären oder Softwarecode generieren, da jede Aufgabe auf Beziehungen zurückgreift, die während des Trainings erlernt wurden, und nicht auf einen speziellen Algorithmus für diese Aktivität.\u003C/p\u003E\r\n\u003Ch3\u003EInferenz generiert eine Antwort Token für Token\u003C/h3\u003E\r\n\u003Cp\u003EDie Inferenz beginnt, wenn ein:e Nutzer:in einen Prompt übermittelt oder eine Anwendung eine Anfrage an das Modell sendet. Bei einem LLM wird alles, was das Modell empfängt – einschließlich des Prompts, abgerufener Dokumente, des Konversationsverlaufs und der Systemanweisungen – in Token umgewandelt, kleine Einheiten, die Textteile darstellen. Das Modell wertet diese Token gemeinsam aus, berechnet die Wahrscheinlichkeit jedes möglichen nächsten Tokens in seinem Vokabular und wählt eines gemäß seiner Dekodierungsstrategie aus. Dieses neu generierte Token wird Teil der Eingabe für die folgende Vorhersage, und der Prozess wiederholt sich, bis die Antwort vollständig ist.\u003C/p\u003E\r\n\u003Cp\u003EObwohl dieser Prozess in Millisekunden abläuft, wird die Antwort nicht auf einmal generiert. Jedes Token hängt von den Token ab, die davor kamen, was es dem Modell ermöglicht, Sätze, Absätze und ganze Dokumente inkrementell aufzubauen. Das Ergebnis wirkt oft konversationell, da das Modell die Sequenz basierend auf dem in diesem Moment verfügbaren Kontext kontinuierlich erweitert.\u003C/p\u003E\r\n\u003Cp\u003EDieser probabilistische Prozess erklärt auch, warum identische Prompts nicht immer identische Ergebnisse produzieren. Kleine Unterschiede im Prompt, im unterstützenden Kontext oder in den Modelleinstellungen können die Antwort beeinflussen. Die \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://codesignal.com/learn/courses/behavioral-benchmarking-of-llms/lessons/exploring-temperature-sensitivity-in-llm-outputs\"\u003ETemperatur\u003C/a\u003E ändert die Form der Token-Wahrscheinlichkeitsverteilung des Modells – niedrigere Werte erzeugen im Allgemeinen konsistentere Antworten, während höhere Werte eine breitere Palette möglicher Ergebnisse zulassen.\u003C/p\u003E\r\n\u003Ch3\u003EKontext prägt jeden Modellaufruf\u003C/h3\u003E\r\n\u003Cp\u003EEin häufiges Missverständnis über generative KI ist, dass das Modell in erster Linie die Qualität der Antwort bestimmt. In der Praxis haben die während der Inferenz bereitgestellten Informationen oft genauso viel Einfluss. „Auf Unternehmensebene ist das schwierigste Problem nicht mehr allein die Modellintelligenz, sondern der Kontext“, \u003Ca href=\"https://www.snowflake.com/de/blog/snowflake-for-ai-enterprise-ai-platform/\"\u003Esagt Baris Gultekin\u003C/a\u003E, Snowflakes Vice President of Product, AI. Ein leistungsfähiges Foundation-Modell bietet umfassende Reasoning- und Generierungsfunktionen, aber die umgebende Anwendung bestimmt, welche Geschäftsinformationen, Richtlinien und Tools für jede Anfrage verfügbar sind.\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-06398fa651","alignment":"left","layout":"simple","quoteSourceName":"Baris Gultekin","quoteSourceTitle":"VP of Product, AI at Snowflake","showQuoteIcon":true,"quote":"Auf Unternehmensebene ist das schwierigste Problem nicht mehr allein die Modellintelligenz, sondern der Kontext.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"text_how-does-generative-ai-work_1":{"id":"text-4ab462d657","text":"\u003Cp\u003EJedes Modell verfügt über ein Kontextfenster, das sich auf die Menge an Informationen bezieht, die es während einer einzelnen Anfrage berücksichtigen kann. Dieser Kontext kann den Prompt der Nutzer:innen, den bisherigen Gesprächsverlauf, abgerufene Unternehmensdokumente, strukturierte Geschäftsdaten, Tool-Ausgaben und Systemanweisungen umfassen, die das Verhalten des Modells steuern. Zusammen definieren diese Eingaben, was das Modell für diese spezifische Interaktion weiß.\u003C/p\u003E\n\u003Cp\u003EDie Bereitstellung von mehr Informationen verbessert jedoch nicht zwangsläufig das Ergebnis. Ein großes Kontextfenster gibt einer Anwendung mehr Spielraum, um relevante Belege zu liefern, erhöht aber auch die Wahrscheinlichkeit, dass wichtige Details unter nicht zusammenhängenden Informationen begraben werden. Genaue, aber irrelevante Dokumente können das Modell von der eigentlichen Aufgabe ablenken, während veraltete oder widersprüchliche Informationen zusätzliche Möglichkeiten für falsches Reasoning schaffen. Effektive KI-Systeme konzentrieren sich daher nicht nur auf die Erweiterung von Kontextfenstern, sondern auch auf die Auswahl, Organisation und Priorisierung der Informationen, die in jeden Modellaufruf einfließen.\u003C/p\u003E\n\u003Cp\u003EDiese Herausforderung ist zu einem der entscheidenden Engineering-Probleme in der Unternehmens-KI geworden. Anstatt jedes verfügbare Dokument an ein Modell zu senden, rufen Anwendungen zunehmend nur die für die Anfrage relevantesten Informationen ab, organisieren sie in einem nützlichen Kontext und setzen Sicherheitskontrollen durch, bevor das Modell überhaupt mit der Generierung einer Antwort beginnt. Diese Techniken sind für produktive KI-Systeme von zentraler Bedeutung geworden und erklären, warum Unternehmensanwendungen von weit mehr als nur dem Foundation-Modell abhängen.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_how-does-generative-ai-work_0":{"id":"text-84f4ff0ea1","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003EKURZER TIPP\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EMehr Kontext ist nicht immer besser. Priorisieren Sie die relevantesten, aktuellsten und maßgeblichsten Informationen, anstatt das Kontextfenster mit jedem verfügbaren Dokument zu füllen.\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-adc1f8d15b","additionalClasses":"anchor-title anchor-title--what-can-generative-ai-create","type":"heading2","lines":["Was kann generative KI erstellen?"],":type":"snowflake-site/components/title-v2"},"text_what-can-generative-ai-create_0":{"id":"text-0b179cbe48","text":"\u003Cp\u003EGenerative KI wird oft mit Chatbots und Bildgeneratoren in Verbindung gebracht, aber das sind nur zwei Beispiele für ein viel breiteres Spektrum an Funktionen. Moderne Foundation-Modelle generieren viele verschiedene Arten von Inhalten, und zunehmend kann ein einziges multimodales Modell über mehrere Formate hinweg innerhalb derselben Anwendung arbeiten.\u003C/p\u003E\n\u003Ch3\u003EText und natürliche Sprache\u003C/h3\u003E\n\u003Cp\u003EDie Textgenerierung bleibt die am weitesten verbreitete Anwendung von generativer KI. LLMs können E-Mails entwerfen, lange Berichte zusammenfassen, Fragen beantworten, Dokumente übersetzen, technische Konzepte erklären und konversationelle Antworten in natürlicher Sprache generieren.\u003C/p\u003E\n\u003Cp\u003EIn Unternehmen unterstützen diese Funktionen zunehmend Wissensmanagement, Dokumentenanalyse, Kundensupport, Suche, Business Intelligence und Produktivitätsanwendungen.\u003C/p\u003E\n\u003Ch3\u003ESoftwarecode\u003C/h3\u003E\n\u003Cp\u003EGenerative KI ist zu einem wichtigen Tool für die Softwareentwicklung geworden. Coding-Assistenten können Boilerplate-Code generieren, unbekannte Funktionen erklären, Refactoring-Möglichkeiten vorschlagen, Unit-Tests erstellen und Code zwischen Programmiersprachen übersetzen. \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/agents/coding-agents/\"\u003ECoding-Agenten\u003C/a\u003E führen Reasoning über ganze Repositories hinweg durch und helfen Entwickler:innen, Abhängigkeiten zu verstehen, relevante Dateien zu identifizieren und koordinierte Änderungen über mehrere Komponenten hinweg vorzuschlagen.\u003C/p\u003E\n\u003Cp\u003EDiese Systeme machen Software-Engineering-Expertise nicht überflüssig. Entwickler:innen überprüfen weiterhin generierten Code, validieren die Korrektheit und bewerten Architekturentscheidungen. Stattdessen reduziert generative KI die Zeit, die für sich wiederholende Implementierungsaufgaben aufgewendet wird, und hilft Ingenieur:innen, effektiver in komplexen Codebasen zu arbeiten.\u003C/p\u003E\n\u003Ch3\u003EBilder, Audio und Video\u003C/h3\u003E\n\u003Cp\u003EBildgenerierungsmodelle erstellen Illustrationen, Produktkonzepte, Marketing-Assets und fotorealistische Bilder aus Beschreibungen in natürlicher Sprache. Ähnliche Techniken unterstützen Bildbearbeitung, Hintergrundersetzung, Inpainting und Style-Transfer, sodass vorhandene Inhalte sowohl geändert als auch von Grund auf neu generiert werden können.\u003C/p\u003E\n\u003Cp\u003EGenerative KI hat sich auch auf Audio und Video ausgeweitet. Moderne Modelle synthetisieren Sprache, generieren Musik, klonen Stimmen, erstellen Soundeffekte und produzieren zunehmend realistische Videos aus Text, Bildern oder kurzen Clips. Während sich diese Funktionen weiterhin schnell verbessern, werfen sie auch wichtige Fragen zu Authentizität, geistigem Eigentum und verantwortungsvoller Nutzung auf.\u003C/p\u003E\n\u003Ch3\u003EStrukturierte und synthetische Daten\u003C/h3\u003E\n\u003Cp\u003ENicht jedes generative Modell produziert für Menschen lesbare Inhalte. Einige generieren strukturierte Ausgaben wie SQL-Abfragen, JSON-Dokumente, API-Anfragen oder Workflow-Definitionen, die Softwaresysteme direkt ausführen können. Andere erstellen synthetische Daten, die echten Datasets ähneln sollen. Wenn sie richtig generiert, evaluiert und vor Memorization geschützt werden, können synthetische Daten Tests, Modellentwicklung und datenschutzsensible Forschung unterstützen.\u003C/p\u003E\n\u003Cp\u003EDie strukturierte Generierung ist besonders wichtig geworden, da KI-Anwendungen mit Datenbanken, APIs und Geschäftssystemen interagieren. Anstatt nur Fragen zu beantworten, können Modelle die strukturierten Informationen generieren, die zum Abrufen von Daten, Aufrufen von Tools oder Automatisieren von Teilen eines Geschäfts-Workflows erforderlich sind.\u003C/p\u003E\n\u003Ch3\u003EMultimodale Ausgaben\u003C/h3\u003E\n\u003Cp\u003EZunehmend verschwinden die Grenzen zwischen diesen Ausgabetypen. Ein multimodales Modell könnte ein Diagramm analysieren, die Ergebnisse in natürlicher Sprache erklären, Python-Code generieren, um die Visualisierung neu zu erstellen, und eine Präsentation produzieren, die die Ergebnisse zusammenfasst – alles innerhalb derselben Konversation.\u003C/p\u003E\n\u003Cp\u003EDiese Konvergenz verändert die Art und Weise, wie Unternehmen über KI-Anwendungen denken. Anstatt separate Systeme für Sprache, Bildverarbeitung und Audio bereitzustellen, bauen Unternehmen zunehmend Workflows um Modelle herum auf, die Reasoning über mehrere Informationsformen hinweg durchführen. Da diese Funktionen erweitert werden, wird die Qualität des zugrunde liegenden Geschäftskontexts noch wichtiger.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_data-for-generative-ai":{"id":"title-v2-ac4d151583","additionalClasses":"anchor-title anchor-title--data-for-generative-ai","type":"heading2","lines":["Daten für generative KI"],":type":"snowflake-site/components/title-v2"},"text_data-for-generative-ai_0":{"id":"text-34b8f2dbd9","text":"\u003Cp\u003EDas Training verleiht einem Foundation-Modell breite Funktionen, aber es gibt dem Modell kein aktuelles Wissen über das Geschäft eines Unternehmens. Kundendatensätze, Lagerbestände, Verträge, Produktdokumentation, \u003Ca href=\"https://www.snowflake.com/de/data-governance/policy/\"\u003EGovernance-Richtlinien\u003C/a\u003E und Finanzdaten ändern sich kontinuierlich. Selbst wenn Teile dieser Informationen in den Trainingsdaten eines Modells enthalten waren, können sie jetzt aufgrund von Sicherheits- und Datenschutzanforderungen veraltet, unvollständig oder unzugänglich sein.\u003C/p\u003E\n\u003Cp\u003EBevor ein Modell eine Antwort generiert, identifiziert die Anwendung in der Regel die für die Anfrage relevanten Informationen, überprüft, ob die Nutzer:innen die Berechtigung haben, darauf zuzugreifen, und fügt diese Informationen in den Kontext des Modells ein. Die Qualität der Antwort hängt nicht nur von den Reasoning-Fähigkeiten des Modells ab, sondern auch von der Qualität, Relevanz und Governance der zur Inferenzzeit bereitgestellten Informationen.\u003C/p\u003E\n\u003Ch3\u003ETrainingsdaten etablieren allgemeine Funktionen\u003C/h3\u003E\n\u003Cp\u003EJedes Modell für generative KI beginnt mit Trainingsdaten. Foundation-Modelle lernen statistische Beziehungen aus riesigen Sammlungen öffentlich verfügbarer, lizenzierter und in einigen Fällen von Menschen erstellter oder synthetischer Inhalte. Die Zusammensetzung dieser Daten beeinflusst alles, von der Sprachkompetenz und den Programmierfähigkeiten bis hin zur mehrsprachigen Performance und dem Domänenwissen.\u003C/p\u003E\n\u003Cp\u003ETrainingsdaten erfüllen jedoch einen grundlegend anderen Zweck als Unternehmensdaten. Ihre Rolle besteht darin, allgemeine Muster zu lehren, die es einem Modell ermöglichen, kohärente Ausgaben über viele verschiedene Aufgaben hinweg zu generieren. Sobald das Training abgeschlossen ist, ändert sich das Wissen des Modells nicht automatisch, wenn neue Dokumente geschrieben werden oder sich Geschäftsinformationen weiterentwickeln. Die Aktualisierung dieses Wissens erfordert in der Regel zusätzliches Training oder Fine-Tuning, die beide rechenintensiv und für sich häufig ändernde Informationen unpraktisch sind.\u003C/p\u003E\n\u003Ch3\u003EUnternehmensdaten bieten Geschäftskontext\u003C/h3\u003E\n\u003Cp\u003EEin:e Kund:in, der/die nach einer Bestellung fragt, ein:e Analyst:in, der/die den Quartalsumsatz untersucht, und ein:e Ingenieur:in, der/die Fehler in einer Anwendung behebt, benötigen alle Informationen, die außerhalb des Foundation-Modells existieren. KI-Anwendungen für Unternehmen rufen diese Informationen zur Inferenzzeit ab, sodass das Modell Reasoning über aktuelle, maßgebliche Daten durchführen kann, anstatt sich ausschließlich auf das zu verlassen, was es während des Trainings gelernt hat.\u003C/p\u003E\n\u003Cp\u003ERetrieval ist zu einem der definierenden Architekturmuster in der Unternehmens-KI geworden, da es allgemeines Reasoning von Geschäftswissen trennt. Anstatt ein Modell kontinuierlich neu zu trainieren, wenn sich Daten ändern, fragen Anwendungen operative Datenbanken, Data Warehouses, Dokumenten-Repositories und Wissensdatenbanken unmittelbar vor dem Aufruf des Modells ab. Die abgerufenen Informationen werden Teil des Kontexts für diesen Modellaufruf, sodass Antworten aktuelle Geschäftsinformationen widerspiegeln können, während das zugrunde liegende Modell unverändert bleibt.\u003C/p\u003E\n\u003Cp\u003EDiese Architektur unterstützt auch eine stärkere Governance. Da Daten in Unternehmenssystemen verbleiben, anstatt dauerhaft in Modellgewichte eingebettet zu werden, behalten Unternehmen eine größere Kontrolle über Berechtigungen, Lineage, Aufbewahrungsrichtlinien und Auditing. Aktualisierungen eines Richtliniendokuments oder Produktkatalogs werden verfügbar, sobald die Retrieval-Schicht auf die neueste Version zugreift, anstatt darauf zu warten, dass das Modell neu trainiert wird.\u003C/p\u003E\n\u003Ch3\u003EEmbeddings und Vektorsuche helfen beim Abrufen relevanter Informationen\u003C/h3\u003E\n\u003Cp\u003EDas Finden der richtigen Informationen ist oft schwieriger als der Zugriff darauf. Die traditionelle Stichwortsuche funktioniert gut, wenn Nutzer:innen genau wissen, welche Wörter in einem Dokument vorkommen, aber Fragen in natürlicher Sprache stimmen selten perfekt mit gespeichertem Text überein. Jemand, der fragt: „Warum ist der Umsatz in Europa gesunken?“, verwendet möglicherweise nicht dieselbe Terminologie, die in einem Quartalsbericht oder Vertriebs-Dashboard vorkommt.\u003C/p\u003E\n\u003Cp\u003EModerne KI-Anwendungen verlassen sich zunehmend auf Embeddings, um diese Lücke zu schließen. Ein Embedding ist eine numerische Darstellung von Daten, die eher die semantische Bedeutung als den genauen Wortlaut erfasst. Text, der „Kundenabwanderung“ diskutiert, kann sich beispielsweise in der Nähe von Inhalten über „Abonnentenkündigungen“ befinden, obwohl die Phrasen nur wenige gemeinsame Wörter haben. Ähnliche Ideen nehmen benachbarte Positionen innerhalb eines hochdimensionalen Vektorraums ein, wodurch sie leichter basierend auf der Bedeutung als auf dem wörtlichen Text abgerufen werden können.\u003C/p\u003E\n\u003Cp\u003EDiese Vektoren werden typischerweise in einem Vektorindex oder einer Vektordatenbank gespeichert, sodass Anwendungen Dokumente identifizieren können, die semantisch mit der Anfrage von Nutzer:innen zusammenhängen. Anstatt nach übereinstimmenden Schlüsselwörtern zu suchen, sucht die Anwendung nach benachbarten Vektoren, die ähnliche Konzepte darstellen. Die abgerufenen Passagen werden dem Modell dann als zusätzlicher Kontext bereitgestellt, bevor die Generierung beginnt.\u003C/p\u003E\n\u003Cp\u003EEmbeddings verbessern die Fähigkeit der Anwendung, relevanten Kontext auszuwählen. Infolgedessen sind sie zu einer grundlegenden Komponente von \u003Ca href=\"https://www.snowflake.com/de/fundamentals/rag/\"\u003ERetrieval-Augmented Generation\u003C/a\u003E (RAG), semantischer Suche und vielen KI-Agenten geworden, die Reasoning über Unternehmenswissen durchführen.\u003C/p\u003E\n\u003Ch3\u003EContext Engineering bestimmt, was das Modell während der Inferenz weiß\u003C/h3\u003E\n\u003Cp\u003EDie aus Unternehmenssystemen abgerufenen Informationen werden Teil des Kontexts des Modells – der Sammlung von Eingaben, die während einer einzelnen Inferenzanfrage verfügbar sind. Dieser Kontext kann Benutzeranweisungen, abgerufene Dokumente, strukturierte Geschäftsdaten, den bisherigen Gesprächsverlauf, Tool-Ausgaben und System-Prompts umfassen, die das Verhalten des Modells steuern.\u003C/p\u003E\n\u003Cp\u003EContext Engineering ist zu einer eigenständigen Disziplin geworden. Anwendungen müssen entscheiden, welche Informationen relevant sind, doppelte oder widersprüchliche Quellen auflösen, wichtige Details bewahren, während sie innerhalb der Grenzen des Kontextfensters bleiben, und sicherstellen, dass sensible Informationen nur mit autorisierten Nutzer:innen geteilt werden. Da Anwendungen für generative KI immer ausgefeilter werden, verlagert sich ein Großteil des Engineering-Aufwands auf die Zusammenstellung eines genauen, relevanten und verwalteten Kontexts für jede Anfrage.\u003C/p\u003E\n\u003Cp\u003EDas Ergebnis ist, dass zwei Anwendungen, die dasselbe Foundation-Modell verwenden, dramatisch unterschiedliche Ergebnisse erzielen können. Der Unterschied liegt oft nicht im Modell selbst, sondern in den Daten, die jede Anwendung abruft, dem Kontext, den sie zusammenstellt, und den Kontrollen, die regeln, wie diese Informationen das Modell erreichen.\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-d637c90b92","additionalClasses":"anchor-title anchor-title--what-are-the-risks-of-generative-ai","type":"heading2","lines":["Was sind die Risiken von generativer KI?"],":type":"snowflake-site/components/title-v2"},"text_what-are-the-risks-of-generative-ai_0":{"id":"text-91de07cd7a","text":"\u003Cp\u003EJede Anwendung für generative KI trifft eine Reihe von Vorhersagen: welches Token als Nächstes kommen soll, welches Dokument am relevantesten abzurufen ist, welches Tool aufgerufen werden soll und in fortgeschritteneren Systemen, welche Aktionen ausgeführt werden sollen, um eine Aufgabe abzuschließen. Diese Vorhersagen machen generative KI bemerkenswert flexibel, aber sie führen auch Unsicherheiten ein, die herkömmliche Software nicht bewältigen muss.\u003C/p\u003E\n\u003Cp\u003EDer Bericht \u003Ca href=\"https://www.snowflake.com/de/lp/radical-roi-generative-ai/\"\u003EROI of Gen AI and Agents 2026\u003C/a\u003E von Snowflake zeigt, dass 40 % der Befragten Datenqualität und -quantität als große Herausforderung nannten, wobei 65 % angaben, dass sie Schwierigkeiten hatten, KI-Datensilos aufzubrechen. 56 % meldeten mindestens eine Governance- oder Compliance-Herausforderung.\u003C/p\u003E\n\u003Ch3\u003EHalluzinationen treten auf, wenn Modelle plausible, aber nicht unterstützte Informationen generieren\u003C/h3\u003E\n\u003Cp\u003EModelle für generative KI sind darauf optimiert, kohärente, kontextuell angemessene Antworten zu produzieren. Sie sind nicht in der Lage zu bestimmen, ob jede von ihnen generierte Aussage sachlich richtig ist. Wenn einem Modell ausreichende Beweise fehlen oder es den verfügbaren Kontext falsch interpretiert, kann es Informationen produzieren, die zuversichtlich und gut geschrieben erscheinen, aber nicht unterstützt oder falsch sind. Diese erfundenen oder ungenauen Ausgaben werden allgemein als Halluzinationen bezeichnet.\u003C/p\u003E\n\u003Cp\u003EHalluzinationen können aus verschiedenen Gründen auftreten. Ein Modell kann über unvollständiges Wissen verfügen, weil die relevanten Informationen nie in seinen Trainingsdaten enthalten waren. Oder die Anwendung ruft veraltete oder irrelevante Dokumente ab, sodass das Modell aus unvollständigen Beweisen schlussfolgern muss. Lange Kontextfenster können auch konkurrierende oder widersprüchliche Informationen einführen, während mehrdeutige Prompts das Modell dazu ermutigen können, Lücken mit statistisch plausibler Sprache zu füllen.\u003C/p\u003E\n\u003Cp\u003ENicht jede Halluzination hat die gleichen Konsequenzen. Das akzeptable Maß an Unsicherheit hängt von der Entscheidung ab, die das System unterstützt, und von den potenziellen Auswirkungen einer falschen Antwort.\u003C/p\u003E\n\u003Ch3\u003ESicherheit und Datenschutz gehen über das Modell hinaus\u003C/h3\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/business/enterprise/\"\u003EUnternehmens-KI\u003C/a\u003E-Systeme arbeiten mit Informationen, die häufig Kundendatensätze, Finanzdaten, geistiges Eigentum und andere sensible Assets umfassen. Der Schutz dieser Informationen erfordert mehr als die Auswahl eines sicheren Modellanbieters. Jede Phase der Anwendung – vom Abrufen von Daten über das Generieren einer Antwort bis hin zum Speichern des Gesprächsverlaufs – muss die bestehenden Sicherheits- und Zugriffsrichtlinien des Unternehmens durchsetzen.\u003C/p\u003E\n\u003Cp\u003EDiese Anforderung wird besonders wichtig, wenn KI-Anwendungen die Fähigkeit erlangen, externe Tools zu verwenden oder Aktionen im Namen von Benutzer:innen auszuführen. Ein Modell, das eine Datenbank abfragen, einen Kundendatensatz aktualisieren oder Code ausführen kann, erbt die Berechtigungen, die von der umgebenden Anwendung gewährt werden. Schlecht konzipierte Integrationen können sensible Informationen offenlegen oder es dem Modell ermöglichen, Aktionen auszuführen, die über das hinausgehen, wozu die anfragenden Benutzer:innen autorisiert sein sollten.\u003C/p\u003E\n\u003Cp\u003EEffektive KI-Sicherheit baut auf vertrauten Sicherheitsprinzipien auf. Identität, Authentifizierung, Autorisierung, Verschlüsselung, Überwachung und Auditing bleiben wesentliche Kontrollen. Der Unterschied besteht darin, dass sich diese Kontrollen nun zusätzlich zu herkömmlichen Anwendungen auf Modellaufrufe, Abrufsysteme und KI-Workflows erstrecken.\u003C/p\u003E\n\u003Ch3\u003EBias und geistiges Eigentum erfordern eine kontinuierliche Aufsicht\u003C/h3\u003E\n\u003Cp\u003EGenerative KI spiegelt Muster wider, die in den zu ihrem Training verwendeten Daten und den während der Inferenz bereitgestellten Informationen gefunden werden. Wenn diese Quellen historischen Bias, unvollständige Repräsentation oder widersprüchliche Standpunkte enthalten, können die Ausgaben des Modells diese Muster verstärken. Ebenso können generierte Inhalte unter bestimmten Umständen urheberrechtlich geschütztem Material ähneln oder sensible Informationen reproduzieren, was rechtliche und ethische Fragen aufwirft, die Unternehmen bewerten müssen, bevor sie KI in großem Maßstab einsetzen.\u003C/p\u003E\n\u003Cp\u003EFür diese Bedenken gibt es selten eine rein technische Lösung. Unternehmen etablieren zunehmend \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/ai-governance/\"\u003EKI-Governance\u003C/a\u003E-Prozesse, die akzeptable Anwendungsfälle definieren, Modelle auf Fairness und Zuverlässigkeit bewerten, dokumentieren, wie KI-Systeme entwickelt werden, und ihr Verhalten nach der Bereitstellung überwachen. Menschliche Aufsicht bleibt besonders wichtig, wenn KI Entscheidungen unterstützt, die Kund:innen, Mitarbeiter:innen, das Gesundheitswesen, das Finanzwesen oder andere regulierte Aktivitäten betreffen.\u003C/p\u003E\n\u003Ch3\u003EDie Rolle der Governance\u003C/h3\u003E\n\u003Cp\u003EGovernance muss der Interaktion über den gesamten Workflow hinweg folgen. Dieselben Zugriffskontrollen, Maskierungsrichtlinien, Audit-Trails und Überwachungsanforderungen sollten gelten, unabhängig davon, ob Benutzer:innen Daten direkt abfragen oder ein KI-Agent auf diese Daten zugreift und eine Aktion im Namen der Benutzer:innen ausführt.\u003C/p\u003E\n\u003Cp\u003EDas angemessene Maß an Governance hängt davon ab, was das System tun darf. Das Zusammenfassen interner Dokumentation, das Empfehlen einer Marketing-Schlagzeile, das Genehmigen eines Kreditantrags und das Ausführen einer Finanztransaktion bergen alle unterschiedliche Risikostufen, selbst wenn dasselbe Foundation-Modell zu jedem Workflow beiträgt.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_evaluation-and-guardrails-help-ai-systems-perform-reliably":{"id":"title-v2-cb225db805","additionalClasses":"anchor-title anchor-title--evaluation-and-guardrails-help-ai-systems-perform-reliably","type":"heading2","lines":["Evaluierung und Guardrails helfen KI-Systemen, zuverlässig zu funktionieren"],":type":"snowflake-site/components/title-v2"},"text_evaluation-and-guardrails-help-ai-systems-perform-reliably_0":{"id":"text-fb44f63697","text":"\u003Cp\u003EDie Evaluierung kombiniert häufig automatisierte Tests mit menschlichem Urteilsvermögen. Teams vergleichen generierte Antworten mit vertrauenswürdigen Referenzdaten, messen die sachliche Genauigkeit und Relevanz, bewerten die Befolgung von Anweisungen und überwachen die Konsistenz über verschiedene Prompts und Szenarien hinweg. Die Evaluierung wird in der Regel nach der Bereitstellung fortgesetzt, sodass Modelle, Abrufsysteme und Prompts aktualisiert werden können, ohne die Qualität der Anwendung zu beeinträchtigen.\u003C/p\u003E\n\u003Cp\u003EGuardrails ergänzen die Evaluierung, indem sie definieren, was ein KI-System während des Betriebs tun darf. Sie können den Zugriff auf sensible Daten einschränken, schädliche Inhalte filtern, generierten Code validieren, eine menschliche Genehmigung vor Aktionen mit großen Auswirkungen erfordern oder verhindern, dass ein Agent bestimmte Tools aufruft. 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Die Aufmerksamkeit hat sich zunehmend auf die Komponenten verlagert, die das Modell umgeben: die Daten, die jede Anfrage informieren, der Kontext, der vor der Inferenz zusammengestellt wird, die Governance, die in der gesamten Anwendung angewendet wird, und die Evaluierung, die sicherstellt, dass die Antworten im Laufe der Zeit genau bleiben.\u003C/p\u003E\n\u003Cp\u003EDiese Verschiebung spiegelt eine breitere Veränderung in der Art und Weise wider, wie Unternehmen über KI denken. Wettbewerbsvorteile hängen immer weniger vom Zugang zu einem bestimmten Foundation-Modell ab, sondern vielmehr von der Fähigkeit, KI in großem Maßstab mit vertrauenswürdigen Unternehmensdaten, Geschäftsprozessen und Governance zu verbinden. Modelle werden sich weiter verbessern und neue Architekturen werden entstehen, aber die um sie herum aufgebauten Systeme werden zunehmend bestimmen, wie effektiv Unternehmen generative KI in messbare Geschäftsergebnisse umwandeln.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_where-generative-ai-creates-competitive-advantage_0":{"id":"text-7d371cfcaf","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EWICHTIGSTE ERKENNTNIS\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EDer Wert von generativer KI für Unternehmen hängt von mehr als nur der Modell-Performance ab. Das umgebende System muss Informationen effektiv abrufen, den Zugriff kontrollieren und Ausgaben bewerten.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular 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gestellte Fragen"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-39ac5bff44","additionalClasses":"hub-faq__subheadline","text":"\u003Cp\u003EIhre häufigsten Fragen zu generativer KI – beantwortet von Snowflake-Expert:innen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"hub-faq-accordions",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"simple_snowflake_acc":{"id":"simple-snowflake-accordion-26d66f7997","additionalClasses":"seo-hub__faqs","showDivider":false,"accordionItemsList":[{"title":"Was ist der Unterschied zwischen generativer KI und traditioneller KI?","richText":"\u003Cp\u003ETraditionelle KI 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