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A safe RAG architecture keeps untrusted content out of the prompt where it can do damage, enforces who can read what at the retriever, and limits what the model can do after retrieval.
Most RAG systems I have seen in real projects do the first half and skip the rest. They retrieve, they answer, they go through a security review that mostly stares at the LLM call. That ...
\ Subtitle: A fraud model is only useful if it improves the way a risk team acts on real transactions.
Fraud detection is often described as a machine learning problem. In practice, it is a decision systems problem.
That difference matters.
You can build a model with a strong offline score and still end up with a weak production system. Y...
On one of our projects, we were building microfrontends, and at some point we wanted to add SSR. The reasons were the usual ones: better first paint, fewer layout shifts, real content for crawlers, less JS to load before something appears on screen.
Setting it up turned out to be harder than I expected. There was no obvious out-of-box path that fit our setup, a...