7 stories
An LLM can return a fluent, confident answer that contains unsupported claims, contradicts the retrieved context, or invents facts. Traditional application monitoring won’t catch any...
An AI system can give a confident answer its source data doesn’t support. It can retrieve the wrong document and summarize it fluently, pick the...
An LLM can return valid JSON that your application still can’t use. It can accept a malicious prompt without complaint, break a schema on the...
Writing an AI policy takes a week. Making it hold on the ten-thousandth agent call, in a cloud account nobody on the governance team has...
AI guardrails explained: input, output and tool-call rails, how Guardrails AI, NeMo Guardrails and Llama Guard differ, and how to test that each rail holds.
A support agent reads a customer’s attached PDF. Buried in white text on page four is a sentence the customer never typed and your user...
AI agent guardrails are relatively straightforward to evaluate when an agent only generates text. The decision gets more complicated as soon as the agent can...
Showing 7 of 7 stories
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