Instructions
You don't need to be a machine learning engineer to build an AI product. But you do need to understand the stack well enough to make good decisions — about which model to use, how to structure your infrastructure, where to use no-code tools and where you need custom development, and what your biggest technical unknowns actually are.
The common mistake is either going too deep too early (spending weeks learning things you don't need yet) or staying too shallow (making architectural decisions without understanding the trade-offs). What you need at this stage is a working understanding of the AI stack: how models, tools, and data retrieval connect; what drives cost and latency; and where the meaningful differences between providers and approaches lie.
For most niche copilot build...
