Most teams think they need custom model training to get real context. But in practice? Training is slow, expensive, brittle, and hard to govern. And for 95% of company use cases, you can get better results faster by pulling in your content at runtime using secure RAG, tailored instructions, and task-specific agents.
In this episode of our AI Explainer Series with Box CTO, Ben Kus, we break down:
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When training actually makes sense (rare, high-risk, ultra-specialized workflows)
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Why most companies don’t need it — and how RAG + instructions deliver grounded, auditable answers instantly
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Real patterns from the field: caching, hybrid RAG, and policy-aware prompting to reduce latency, cost, and risk
Ben and I would love to hear from you…
What is one task in your org that feels like it “needs” a custom model — and what problem are you trying to solve with it?
If you’re catching up, you can also check out other highly-requested explainers:
