Why most RAG deployments fail before they go live

MBA Training AI · Jul 20, 2026 · 4m

Retrieval-augmented generation promised to make enterprise AI actually useful on proprietary data. The gap between that promise and production reality reveals a set of specific, fixable problems that most teams keep hitting in the same order.

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Retrieval-augmented generation (RAG): giving models your data

Evaluating outputs: how do you know it works?

Choosing the right approach: prompt, RAG, fine-tune, or agent

Agentic RAG: retrieval inside the agent loop

Cost, latency, and model selection tradeoffs

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