New Model, New Playbook – How I Adapt My AI Habits

Last week, I shared how I’ve been incorporating new AI models into my workflows for Neural Gains Weekly. The more I experiment, the more I realize how important it is to stay current as these models evolve. If you rely on AI at home or at work, staying up to date is no longer optional. Each release can change how a model reasons, how it follows instructions, and what it is actually good at. If you treat every new model like the last one, your results will quietly get worse over time. As the model arms race heats up between the big labs, that gap will only grow. So what can you do when a new model is released? In this week’s Founder’s Corner, I want to share three simple ways I think about learning new models so you can adapt faster and get better outputs as the AI landscape keeps shifting.

Tip 1: Learn What the Model Is Optimized For

Not every model is trained and fine-tuned to excel at the same tasks. That can be hard to see if most of your interactions are simple, search-like chats. It’s a big reason I push myself (and you) to run more complex workflows through AI. Before I judge a model, I want to know what it was actually built to be great at.

When you pay attention to headlines and launch materials, you start to pick up on these cues directly from the labs. One model touts superiority in all things coding, another leans into being the “agentic” leader. I read release notes and listen to a few trusted podcasts to get a sense of where a model is supposed to shine and how it can best support my workflows. Those small habits shorten the learning curve and make it easier to plug the right model into the right task.

Tip 2: Adjust Your Prompts When the Model Changes

The prompt is our chance to provide context, structure, and guidelines that help the AI produce the outcome we want. It’s easy to get comfortable with a certain prompting style or reuse the same prompt over and over again. But this can lead to poor outputs when a new model is introduced. New models are not just updates, they behave like completely new systems that often require reengineered inputs.

Luckily, the process to learn the prompting structure for a new model is straightforward. You might need to be more explicit about structure, set length constraints, or change how you chunk information, but you don’t have to figure this out alone. I often ask the model directly for advice on how it wants to be prompted for a specific task and use that as a starting point. From there, I let the AI help me refine a “best prompt” for the workflow I am testing, which removes a lot of the guesswork. Over time, this builds a mental playbook for each model instead of forcing one-size-fits-all prompts on every new release.

Tip 3: Run Small “Same Task, Different Model” Experiments

Experimentation is crucial in the world of AI, especially when you are trying to learn a new model. The simplest way to do this is to run the same real task through different models and compare the results. You do not need benchmarks or lab tests, just a light habit of asking, “Which model handled this better, and why?”.

I started comparing outputs from ChatGPT 5.0 and ChatGPT 5.1 as soon as the newer model launched. I wanted to see if I could spot patterns that highlighted the differences between the two. The “AI Education” prompt was a great starting point to compare which model’s output aligned with my vision, needed the least editing, and felt the most useful for the audience. It was obvious how much better 5.1 handled the task, even though I was using the same prompt I had originally optimized for 5.0. That simple experiment helped me compare the models directly and choose the best content for the newsletter. Over time, that kind of side-by-side testing builds a feel for each model that no release note can give you.

At this point, I see new models less as shiny toys and more as part of the foundation of my work. The models will keep changing, and if my habits do not change with them, my results will slowly fall behind. This rings true in the workplace as more employers start mandating the use of AI. We will all need to be educated and well-versed in the models that power the tools we use every day. My perspective is that learning how new models work will be the equivalent of learning to use email in the early 2000s: mandatory. The models will keep getting smarter, but the real leverage comes from how quickly we learn them and fold them into the work that matters most to us.