As large language models (LLMs) power more agentic systems capable of performing autonomous actions, tool use, and reasoning, enterprises are drawn to their flexibility and low inference costs. This growing autonomy elevates risks, introducing goal misalignment, prompt injection, unintended behaviors, and reduced human oversight, making the incorporation of robust safety measures paramount.
]]>Large language models (LLMs) have demonstrated remarkable capabilities, from tackling complex coding tasks to crafting compelling stories to translating natural language. Enterprises are customizing these models for even greater application-specific effectiveness to deliver higher accuracy and improved responses to end users. However, customizing LLMs for specific tasks can cause the model…
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