How to Stop Over-Engineering with AI When a Simple Query Will Do

I spent three days last month building an AI-powered search feature for an internal tool. Embeddings, vector database, retrieval-augmented generation — the whole stack. My teammate looked at it and said, "Couldn't you just use PostgreSQL full-text search?" He was right. The dataset was 12,000 records with well-structured fields. I'd built a Ferrari to drive to the mailbox. If you've caught yourself reaching for an LLM API or embedding model before even considering whether the problem needs it, this post is for you. Let's walk through how to diagnose over-engineering with AI and apply the right tool for the job. The Root Cause: Solution-First Thinking The core problem isn't AI itself — it's that we've started working backwards. Instead of asking "what does this feature need to do?" we're asking "how can I use AI here?" I've done it. You've probably done it too. This leads to three predictable failure modes: Unnecessary latency — an LLM call adds 500ms-3s to something that could resolve