AI workflows: an industry optimising the wrong variables
The article discusses the current state of AI workflows, highlighting the rapid evolution of models and the outdated nature of existing training materials. It emphasizes the need for durable AI solution architecture rather than just optimizing past bottlenecks. The author argues that effective prompt engineering is often less efficient than leveraging the capabilities of the models themselves.
- ▪The advice economy around LLMs is rapidly evolving, with techniques quickly becoming outdated.
- ▪Current AI workflows often focus on optimizing previous bottlenecks instead of developing durable solutions.
- ▪Effective prompt engineering may not be the best approach, as models can provide better, more current answers directly.
Opening excerpt (first ~120 words) tap to expand
Navigating AI with paper mapsAdamMay 21, 2026ShareThe advice economy around LLMs is such a strange thing to watch. Somewhere on LinkedIn this morning, someone with a job title that didn't exist eighteen months ago is sharing their hard-won secrets for better LLM output with forty thousand followers. They're not wrong, exactly. The techniques worked — for the model they had, when they found them. The trouble is that "what works" has a shelf life measured in model releases. The post keeps circulating long after it's relevant; the conference talk is accepted months before anyone delivers it.Even the vendors' own training material runs a release or two behind their own models.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Hacker News (AI / LLM).
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