Here’s something a lot of heavy AI users never say out loud: they’re not slow because they can’t prompt. They’re slow because they spend half their day deciding where to send the prompt.
Claude for writing. ChatGPT for quick back-and-forth. Perplexity when you need live sources. Gemini for long-context stuff. A cheaper model for the low-stakes tasks.
Not a bad setup. But hidden inside that setup is a tax you pay 40 times a day: “Where should this go?” And it doesn’t feel like a tax because each individual decision takes maybe five seconds. The problem is that five seconds of context-switching, multiplied by dozens of prompts a day, adds up to a real drag on your thinking. You keep breaking focus, re-orienting, second-guessing yourself. That’s not a prompting problem. That’s a routing problem.
And it compounds faster than bad prompts do.
The Old Way vs. The Smarter Way
The old mental model: pick a favorite model and force everything through it. Or worse, make a fresh routing decision every single time from scratch, mid-task, right when you’re in flow. Both approaches have the same flaw. You’re treating every prompt like it’s a unique situation that requires fresh judgment, when actually most prompts fall into a small number of repeating categories.
The smarter mental model: prompts have types. Route by type, not by gut feel. This is how experienced engineers think about systems. You don’t evaluate every database query from scratch. You have patterns. You apply them. The decision cost drops to near zero because you made the decision once, not every single time.
Once you see it, you can’t unsee it.
The 5 Prompt Types Worth Knowing
- 🖊️ Writing prompts need taste and tone, not just accuracy. Send these to models trained hard on long-form language. A blunt model gives you something technically correct and completely forgettable. Think about the difference between asking a model to “write a paragraph explaining X” and getting back something that reads like a Wikipedia stub versus something that actually sounds like a person with a point of view. The model matters here more than the prompt.
- 🔍 Search-heavy prompts need live data. If facts could have changed in the last six months, sending this to a model without web access is just outsourced guessing. Use a search-grounded model. This applies more often than people think: market data, current regulations, recently released tools, company news, pricing. The model’s training cutoff is a hard wall, and confidently wrong is worse than uncertain.
- 💻 Coding and debugging prompts need precision and context tracking. The wrong model here doesn’t just give bad code. It gives confidently wrong code that costs you 20 minutes debugging the debugger. For anything with real complexity, multi-file context, or tight correctness requirements, you want a model that holds state well and hedges when it’s uncertain instead of just filling in plausible-looking gaps.
- Simple utility prompts need speed, not brilliance. Summarize this. Reformat that list. Extract the key dates from this document. Using a premium model for that is like hiring a senior engineer to rename a file. You’re paying for capability you don’t need, and you’re waiting longer than necessary. A fast, cheap model handles 80% of everyday tasks without any drop in quality you’d actually notice.
- 🧪 Second-opinion prompts are the most underused type. Don’t ask another model to answer your question. Ask it to challenge the first model’s answer. Give it the original response and say: “What’s weak about this? What’s missing? Where would this break?” That one shift alone is worth building into your workflow. Two models agreeing doesn’t mean the answer is right. But two models disagreeing tells you exactly where to look harder.
The Routing Rules That Actually Hold Up
Once you sort by type, the decisions get fast:
- Facts that might have changed? Search-first model. No exceptions, even if you think you remember the answer.
- Tone matters? Writing-strong model. Speed doesn’t compensate for flat copy.
- Small, fast task? Cheap and quick. Save the premium calls for the work that actually benefits from them.
- Output actually matters? Run a second model as critic. Takes 90 seconds and catches the things you stopped noticing after staring at the first draft.
- Model starts fighting the task? Switch early. Don’t try to prompt your way out of a bad fit.
That last one is the biggest unlock. Sometimes the problem isn’t your prompt. Sometimes you’re just asking the wrong model, and no amount of rephrasing will fix a fundamental capability mismatch. The sooner you recognize that feeling, where the model keeps drifting off-target or hedging in ways that don’t help, the less time you waste trying to coax a square peg into a round hole.
Start Here
Next time you open a chat window, pause two seconds and ask: “What type of prompt is this?”
That one question replaces 40 micro-decisions a day. It removes the friction that happens between “I have a task” and “I’m actually working on it.” And it makes every model you already use feel noticeably sharper, because you’re actually using each one for what it’s good at rather than defaulting to whatever tab you opened last.
You don’t need new tools. You don’t need a fancier stack. You need a routing layer in your head that runs automatically.
The skill isn’t finding the best model. It’s knowing which model is best for this.
Frequently Asked Questions
Q: The categories in your post feel kind of broad. How do I know if I’m routing a ‘writing’ task to the right model?
A good catch. One commenter pointed out that ‘writing’ is too loose , ‘drafting from an outline,’ ‘critiquing an existing draft,’ ‘summarizing,’ and ‘rewriting for a new audience’ can all need different models. The more specific you get with your categories, the more reliable your routing actually is.
Q: How do I decide which model to pick if I don’t already know the differences between them?
Start by noticing where each one trips up on your work. Does Claude sometimes miss the nuance you want? Does ChatGPT ramble? Does Perplexity hallucinate dates? Once you know how each model fails on the tasks you actually do, your routing decisions become a lot faster and more confident.
Q: Are there other tools beyond Claude, ChatGPT, and Perplexity for specific prompt types?
Yeah, it’s worth experimenting once you’ve nailed your core categories. One person mentioned using Runnable for heavy creative work like decks and landing pages, and it just works for them. The point isn’t to collect every tool , it’s to find the tool that’s weirdly good at your specific type of work.
Q: At what point do I stop refining my routing system and just… use it?
You’re looking for when it stops feeling like a decision. Start with the categories from the post, use them consistently for a week, and only refine if you keep hitting real friction. Once the routing becomes automatic, you’re done. The goal is to lose the routing tax, not trade it for ‘am I routing correctly?’
I stopped asking which AI model is best. The better question was: what kind of prompt is this?
by u/Medical_Fox_7259 in PromptEngineering