Six Choices Every AI Engineer Has to Make (and Nobody Teaches)

AI engineers face six critical trade-offs in production that are rarely covered in academic courses. These decisions include whether to build a custom solution or buy an API, balancing model complexity against long-term maintainability, and prioritizing data quality over sheer quantity. Other key choices involve selecting between batch and real-time inference, deciding when to use prompt engineering versus more costly fine-tuning, and determining the appropriate level of human oversight for an automated system. These practical considerations directly impact cost, performance, and operational stability for live models.