Originally published on AI Tech Connect.
What you need to know Most teams that put an LLM into production build a quality eval suite — accuracy, faithfulness, helpfulness on the inputs a cooperative user sends. Far fewer build the other half: an adversarial safety eval that stress-tests the same app against a user who is trying to break it. That gap is where the incidents come from. A retrieval assistant that scores 0.9 on faithfulness can still leak another customer's data when someone pastes a crafted instruction into a support ticket. An agent with a shell tool can pass every capability test and then run a destructive command because a web page it fetched told it to. Red-teaming — deliberately attacking your own system before someone else does — is how you find those failures on your terms. This guide is a repeatable…
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