I’m keeping this one short and efficient. Fitting, given the topic.
I’ve been thinking less about what AI can produce (time back, productivity, ROI) and more about how we go about practicing efficiency when we actually put LLMs to work.
Because the way I see most of us use them is overcooked for the work we’re trying to have them produce.
It’s down to a reflex problem.
Somewhere along the way, we reverted to simple evaluation of LLMs. Latest = greatest, bigger = better, and pricier = more valuable. Shiny-object syndrome is brighter than ever, simply the default, and we’ve forgotten how to right-size.
Why has this happened?
Lack of fluent expertise in this space. This stuff is moving so fast, it’s tough to keep up - and let alone form any personal judgement.
Frictionless setup. Top-tier models are one click away and unusually affordable to try (relative to the previous way we could get access to such powerful SaaS).
Models are products. And teams are shipping more and more features that showcase value. Spec sheets of models become taste.
Naming. Pick “thinking” over “mini”? Of course, why would we use something that’s “not as smart”?
This same line of thinking is why expensive reasoning models often end up being used for the most trivial tasks, and how perfectly good models get dismissed as “trash” just a few months after their release. Remember those days with GPT-3.5-turbo? Those were truly the darkest of times… right?
I’m not against advanced, powerful AI models. And I’m not trying to preach for austerity. I’m just a fan of proportion. It’s like how a bigger house doesn’t make a better home by default. And you don’t need a supercar to drive to the grocery store. The same can apply to using AI - big context windows don’t necessarily make better answers, and using reasoning models to look up a simple recipe is wasteful.
However, the problem we face is that the excess is invisible, and the constraints are being lowered. Even as someone who has a business in helping others work properly and more effectively with AI, it’s hard for me to hit usage limits consistently. So the incentive to throttle down to a more efficient model is smaller than ever.
But the costs are real, even if they don’t appear as straightforward: time, energy, attention, creativity, and the resources that go into making this industry run in the first place.
So, here’s the stance I work from, and one I think the world around us would literally thank us for considering (even a little bit): start small unless reality proves otherwise. Not as a forced way of adopting minimalism, but it will keep your attention honest.
Because when these magical tools and applications blow our minds, our attention to the behind-the-scenes aspects and other impacts drifts away. We want more of what we just witnessed. But we then over-credit the tool and find ourselves under-specifying the task to justify it.
In short, “fit > flash” should become the default approach, and any escalation otherwise requires evidence to support it.
To do this for myself (as someone who has fallen into the trap of going too big to accomplish relatively little), I now run four quick questions before I ever turn AI on:
How big is the input? (context I feed it)
What’s the task shape? (straightforward transform of given data vs decisions with tools)
How big is the output? (length and or depth you expect in return)
How fast do I need it to work?
That alone does a good enough job of being more effective than most using AI extensively today. Most pick the latest and greatest and default to smaller models when their allotted usage limit is near complete.
If I feel I don’t have the answers to the above questions and need to build as I go, I’ll follow a similar line of thinking but be more prescriptive:
Pause - do I even need AI? Or is this something a workflow node in n8n can solve? Perhaps some code that can handle a complex regex I’m battling with would suffice.
If I do require AI, small/fast models can be used to extract → transform → format with short in/short out.
Do I need greater structure for more reasoning or a larger context? Move to a mid-tier model that is typically default (e.g., GPT-5)
If I require more advanced reasoning, or I’m setting up agentic work where I need AI to support planning, reconciliations, multi-step choices with tool interactions, I’ll use reasoning models trained for this type of constraint.
For huge inputs or high-stakes analysis, I’ll use more frontier/long-context/specialist models.
However, the key to all of this is to escalate only after prompt fixes, few-shot examples, and evaluations that demonstrate the output is not meeting your criteria. Yes, prompts do operate differently from model to model, and there are best practices for each. But the determination of whether AI is “good” is very sensitive to how you’ve prompted it (not as much the model in most general use cases).
(If you want to know more about doing objective evaluations in n8n and see the impact of changes to prompts vs changes to models, watch below. I’ll be posting more like this on LinkedIn from now on.)
The moral of the story is that you only escalate to a more extensive model if you run into these failures:
Structural: e.g., did the fields map as expected?
Interpretive: e.g., did the justification follow logically?
Operational: e.g., did the outcome reduce intervention requirement?
If a cheaper model misses one of those and you’ve tightened your prompt, then escalate. Otherwise, you’re at risk of being inefficient for comfort.
If your default is “just use the best,” you’re being wasteful in more ways than you think. But if your default is careful, calculated proportion, you become a builder - and that craft scales because it means smaller loops, more apparent failures, and saner bills.
The goal is to be clear about cause and effect. Using AI can be fast and quick, which can give us a false sense of security. Having it work and avoiding the model choice conversation is basically allowing the model to compensate for your lack of craft and care.
And if we genuinely care about preserving creativity, agency, craft, and the resources this industry relies on, thinking frugally about compute becomes more than just saving a buck or being efficient with your AI credit limit. We’d be in a much more sustainable spot with AI if we all became more resource-conscious before we turn things live.
Following the simple mindset and flow I outlined above doesn’t require much effort and can produce a higher ROI than you might expect. Give it a try and let me know how it goes!