Guardrails and Prompt-Injection Defense
The pipeline around the model — input filtering, defense-in-depth against prompt injection, output filtering, and why none of it is a free add-on
A deep dive on the topic every AI Engineer interview probes: guardrails as a pipeline wrapped around the model, not a sentence in the prompt. Covers input filtering (PII redaction, injection classifiers, topic filters), prompt injection as the defining LLM threat and why it has no clean code/data separation, defense-in-depth layers with worked examples, output filtering (schema, groundedness, PII, refusals), the hard limits of any classifier against an adaptive attacker, the latency/cost budget for a guardrail pipeline, and a full worked trace of an attack through every layer.
Practice questions (5)
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Defending a RAG Assistant Against a Poisoned Article
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Designing the Output-Side Guardrails
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A Browsing Agent and an Exfiltration Attempt
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Budgeting a Guardrail Pipeline Under a Latency Target
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The Limits of a Single Classifier
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