Large Language Models (LLMs) hold great promise for enterprises, but their integration comes with significant challenges, including the risk of AI-generated inaccuracies known as “hallucinations.” In this video, we explore the impact of these inaccuracies on businesses and provide strategies to mitigate them.
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Prompt Intelligence is the key to unlocking the full potential of Large Language Models (LLMs) in enterprises.
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Personal Identifiable Information (PII) includes sensitive data such as social security and passport numbers, as well as biometric data like faces and fingerprints. When training or refining Large Language Models (LLMs), there’s a risk of accidentally including PII, leading to significant real-world consequences like identity theft, privacy violations, and financial losses.
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Retrieval-augmented generation (RAG) represents a significant leap in the capabilities of large language models (LLMs). By integrating internal databases and documents, RAG enhances the relevance and accuracy of LLMs, making them subject matter specific.
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In the rapidly evolving AI landscape, a new cybersecurity threat called Prompt Injection is emerging.
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Discover how a cloud native application protection platform (CNAPP) safeguards your cloud native applications and microservices.
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Secure your kubernetes pods with these tips to help protect your application.
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GenAI is full of exciting opportunities, but there are significant obstacles to overcome to fulfill AI’s full potential. Learn what those are and how to prepare.
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Learn how enterprises can leverage RAG alongside LLMs to provide accurate, relevant, and current results in their applications.
