New AI models are shipping all the time, with fresh prompting guides accompanying each launch. The notes from one iteration to the next provide fairly generic instructions: Be more direct. Stop asking it to think step by step. Say each thing once. Trim your examples.
Odds are you’ve got a pile of saved prompts, Claude skills and projects, and the like, so those guides stop reading like advice and start reading like a punch list. Do I have to update all of this? Some of it? Which parts? I’ve built my fair share of skills and prompts, notably a weekly reporting workflow and productivity system, so I have a firsthand understanding of the confusion and frustration.
Most of this used to be somebody else’s problem. When a software application or system upgrade threatened to break something, IT owned the migration. Now, every knowledge worker is the IT department for their own AI setup. There’s no maintenance plan for that custom GPT you built at 11pm on a Sunday, and you are the emergency support tech when it stops working.
I’ve been through this cycle of break/fix a whole bunch of times, and here’s where I’ve landed: Sometimes the model really did get worse for your task. Providers do ship regressions, and Anthropic wrote a whole postmortem about three of them last fall. More often, though, when a prompt breaks on a new model, the prompt you built was doing two jobs to try and bend the AI in the direction you wanted it to generate.
The brief and the tricks
Everything in a prompt is one of two things:
The brief is the actual request. That entails information like who you are, what you need, what context matters, and additional details that inform the standard of quality, e.g. “I run partner marketing at a healthcare software company. Draft a follow-up email to a webinar attendee. Here are two past emails that got replies. Under 150 words, and don’t oversell.”
The tricks are everything you bolted on to make a particular model behave: “Think step by step.” “You are a world-class copywriter.” “Take a deep breath“ (The longer version of that last one was discovered through an LLM-driven prompt-optimization process and improved a particular math-benchmark setting, rather than being generally helpful). The instruction you pasted in three times because it kept getting ignored also falls under this category, as does the weird formatting workaround for a bug that got fixed two releases ago. And, who can forget the classic: MAKE NO MISTAKES!
Your brief is the core of what you want. It describes your work, and your work doesn’t care what the model version is. The tricks are accreted like defunct orbiting satellites from old model weaknesses, which is why the migration guides keep telling you to cut them. They’re usually dead weight at best, but can sometimes actively hinder your productivity. OpenAI’s own guide shows a “be THOROUGH” block that helped older models but made GPT-5 call search over and over, and Anthropic now tells people to rewrite “CRITICAL: You MUST use this tool” as, simply, “Use this tool when.”
Providing examples of good work to an AI tool is a tricky case, because they have the potential to both help and harm the output. The two past emails in the above brief are important, concise, and clear. They set the standard of quality, and they’ll do that job on any model. Shoving ten examples you piled up to force an old model into a format it kept fumbling is a brittle construct that degrades the quality of the output. When a guide says trim your examples, it means the latter kind.
My standard process to audit my prompts is asking one question line by line. Is this describing what I want, or coaxing a particular model into behaving? Structure, output format, and a role you use to customize the output all count as describing (the model makers still recommend them). Rip out the pile of other stuff trying to coax a model; chances are you will get better results.
Put your context in files, not in the prompt
Take the same idea of reducing clutter further. The prompt should be the smallest part of your setup.
Most serious AI tools now give you somewhere to keep context that outlives any single interaction, like project files or uploaded docs. That’s where you should place information that a prompt can reference. For many businesses, that means things like the tone guide, brand standards, your two best report examples, the glossary of internal acronyms nobody outside your company could possibly decode. A new model will still read your files a little differently, but there’s a difference between re-testing a setup you can understand and doing archaeology inside one giant pasted mega-prompt.
Keep a cheap test set
Teams that ship AI software run evals before they swap models. Think of evals as a ‘practice test’ for AI models; a standardized set of tasks used to check if the model still performs reliably after an update. Steal their idea! Save three to five real tasks, each with an output you know was good. Then, when a new model drops, run those tasks before you trust it with live work.
When you’re performing this testing on a new model, it’s important not to grade the new output by how closely it matches your saved one, because a better answer won’t match, and different isn’t worse. Grade it against the brief. And run the task you care about most a few times, since the same model won’t give you the same answer every time.
This is maintenance now
I’d love to tell you this is a one-time fix. It isn’t. AI tooling is personal infrastructure now, and infrastructure wants a little upkeep. When a major model lands, a quick audit grounded in briefs that focus on goals and outcomes should suffice for most people.My weekly reporting workflow has survived the last two model swaps without an edit, because everything in it describes the report instead of managing the model.
If after reading this, you’ve come to the realization that you need to get rid of some old tricks bogging down your set-up, the best time to act is now. The beauty of the situation is that you don’t have to go at it completely alone: you’ll find a willing and eager assistant in Claude, ChatGPT, Gemini, or whatever platform it is you’re using. Just make sure you prompt it correctly.
