Building a demo AI app is simple, but shipping to production requires a completely different approach. A new post from the DEV Community argues that most tutorials ignore the real complexity. Production systems need more than just API calls; they demand robust architecture, cost controls, and a plan for inevitable failures.

The author stresses that developers must stop treating LLMs like reliable functions. A proper stack includes a Backend-for-Frontend (BFF) and a dedicated AI Orchestrator Layer. This layer manages prompt versioning, cost guards, and model routing. Skipping this step leads to technical debt that is difficult to fix later.

Prompt engineering is now a software engineering discipline. Developers should treat prompts as code, using JSON schemas for typed outputs and contract tests. Similarly, Retrieval-Augmented Generation (RAG) requires more than a vector database; it needs hybrid search and aggressive caching. The goal is to manage latency and costs effectively.