09/01/2026
Everyone wants "AI." Almost no one wants to talk about the plumbing. 🔧
I’m seeing a dangerous trend in our industry right now.
Companies are rushing to integrate GenAI. They build a cool prototype in a weekend using an OpenAI API wrapper. It looks like magic. ✨
Then they try to go to production, and reality hits hard:
❌ Latency: The user waits 10 seconds for a response.
❌ Cost: The API bill skyrockets because there is no caching or token optimization.
❌ Hallucinations: The bot starts promising refunds you don't offer.
❌ Compliance: Sensitive customer data is accidentally sent to a public model.
Here is the engineer's perspective:
The model is the easy part.
The hard part is the Engineering around it. It’s the vector databases, the RAG (Retrieval-Augmented Generation) pipelines, the semantic caching, and the governance.
AI isn't magic. It's just another workload that needs robust DevOps and SRE practices.
If you are building an AI feature, stop asking "Which model should we use?" and start asking "How do we engineer the pipeline to feed it?"
Are you building an AI prototype, or an AI product? There is a big difference.