Build a guarded AI pipeline
Create a policy boundary for data, models, outputs, and budgets without handing your application’s authority to a classifier.
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Prompt injection, in practice.
Treat external documents, user content, and tool responses as data rather than trusted instructions. These articles explain practical boundaries around context, retrieval, outputs, and actions, with an emphasis on limiting consequences when a model interprets something incorrectly.
Prompt injection: defend the boundaries
Keep untrusted content separate from authority with scoped retrieval, constrained tools, output checks, and end-to-end tests.
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Give agents boundaries, not blank checks
Keep retrieval permissions current, tool arguments narrow, approvals exact, and persistent memory inside its own trust boundary.
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Make the next request a better one.
Start with a clear architecture. Build toward boundaries you can test.