Most AI teams hit the same walls once they move past prototyping. The RAG pipeline that worked flawlessly in a demo starts hallucinating under real traffic. Inference costs climb without clear optimization levers. GPU resources sit underutilized while workloads spike elsewhere.
Most of the time, the root cause traces back to architecture decisions that weren't pressure-tested for production. This month's DigitalOcean tutorials focus on diagnosing and fixing those failure points across the AI infrastructure stack.
Why RAG Systems Fail in Production
Why do seemingly solid RAG demos collapse under real-world conditions? This article traces failures back to retrieval quality, latency tradeoffs, and embedding drift. You’ll get a clear picture of how upstream decisions—such as chunking strategy and ranking—directly affect downstream LLM outputs. If your team is building production pipelines, evaluation, monitoring, and retrieval engineering matter just as much as model choice.
Dedicated vs. Serverless Inference as You Scale
