Six Choices Every AI Engineer Has to Make (and Nobody Teaches)
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Six production trade-offs that AI engineers face but rarely learn in formal training: build vs. buy for LLMs (API vs. self-hosting cost curves), model complexity vs. maintainability (the hidden 18-month debugging tax), data quantity vs. quality (noise thresholds and data swamps), batch vs. real-time inference (most problems don't need sub-second predictions), prompt engineering vs. fine-tuning (start with prompts, escalate only when needed), and automation vs. human oversight (selective HITL based on error cost and reversibility). Each trade-off is backed by recent research with concrete numbers, such as teams exceeding LLM budgets by 340% due to missing cost attribution, and prompt optimization with DSPy beating fine-tuning by 6–19 points on some benchmarks.
