You send the prompt. The cursor blinks. Claude disappears into its own world for 30 seconds. And you… open a random tab.
That gap is where one developer on r/PromptEngineering had a small revelation. He wasn’t really working most of the time he was “using AI.” He was waiting. Waiting for Claude to finish a task. Waiting for the code edit. Waiting for the response loop to complete. He noticed it mid-session one afternoon: three tabs open, none of them useful, his actual work sitting there in a browser window blinking at him. So he started doing something different: planning the next prompt while the current one ran. Staying one step ahead of the AI completely changes how you build.
The only problem was no tool was built for that. Generic markdown editors exist, but they weren’t designed around AI workflows. No token estimates. No reusable prompt templates. No structure at all. Just a blank box that pretended the writing was the hard part. So he spent a few hours with Claude and built one himself.
🧠 Why This Problem Is Bigger Than It Looks
The idle gap between prompts doesn’t sound like a big deal until you add it up. If you’re doing serious AI-first development, you might run 40 or 50 prompts in a session. Each one comes with a wait window, anywhere from 10 seconds to a couple of minutes depending on what you’re asking. Multiply that across a workday and you’ve burned a meaningful chunk of time doing nothing useful. Some people estimate they’re actively engaged maybe 60% of an AI-heavy session. The rest is dead air.
The smarter move is to parallelize your thinking. While Claude works on the current task, you’re already drafting the next prompt, anticipating the output, planning the follow-up. You go from reactive to one step ahead. That’s not just more efficient. It’s a completely different mental mode. You stop feeling like you’re watching a loading bar and start feeling like you’re conducting something. The AI is doing its part while you’re already two moves ahead.
Most productivity tools miss this entirely because they’re built for linear work. AI workflows aren’t linear. They’re loops: send, wait, review, adjust, repeat. A tool that actually fits that loop changes everything about how a session feels.
⚙️ What the Tool Does
It’s called Prompt Planning, and it lives entirely in your browser. No signup, no cloud storage, nothing to install. Here’s what’s inside:
- Rich text editor: structured writing, not just a plain text box. You can organize prompts with headers, lists, and formatting before they ever reach the AI.
- Token estimation: the cl100k_base tokenizer is built in so you know your prompt size before you send it. No more guessing whether you’re about to hit a context wall mid-conversation.
- Reusable templates: save your common prompt patterns and pull them in fast. If you write the same kind of code review prompt ten times a week, it should take one click, not two minutes of rewriting from scratch.
- Context snippets: store repeatable background info so you’re not copy-pasting the same setup into every conversation. Tech stack, key constraints, tone guidelines. Set them once, reuse every session.
The philosophy is obvious once you see it: remove friction, don’t pile on features for the sake of having features. Every element is there because it solves a real problem in the AI workflow loop, not because a feature checklist looked too short.
💡 Tips for Making It Stick
The tool is only half of it. Here’s how to actually build the habit:
- Draft the next 2-3 prompts while the current one runs. They don’t need to be perfect. Rough notes beat a blank screen, and you’ll refine them once you see the output anyway. The goal is to never be caught flat-footed when Claude finishes and you’re still thinking about what to ask next.
- Keep a context snippet per project. Tech stack, key constraints, important decisions. Set it up once, reuse it every session. Stops you from re-explaining the same background to Claude every single conversation, which, if you’re being honest, you’ve done more times than you’d like to admit.
- Use token estimates as a gut check. If your setup alone is hitting 3k+ tokens, your context is bloated. Trim before you send. Bloated context doesn’t just cost tokens. It dilutes focus, and you’ll often get fuzzier responses as a result.
- Review output while the next prompt is still in draft. Catching a wrong direction early saves you two or three correction rounds downstream. The moment Claude delivers, switch over, assess quickly, then finalize your next move. You’re already halfway there.
🚀 Give It a Shot
Prompt Planning is free and lives at prompt-planning.vercel.app. No account needed, works right in the browser. Takes about three minutes to set up your first context snippet and template. After that, you’ll wonder why you ever spent those wait windows refreshing your inbox.
The waiting isn’t going away. But what you do with that time is entirely up to you.
Frequently Asked Questions
Q: How do I stay productive while waiting for the AI?
Parallelizing your work is the secret. While one AI instance is processing, crack open another prompt window to draft your next request, review code diffs, or plan your follow-up step. Instead of staring at a loading spinner, you’re always one move ahead of the AI.
Q: Why would I need formatting features if the AI won’t see them?
Think of them as your thinking tools, not the AI’s. Headings, callouts, and bold text help you organize your own thoughts and structure context snippets clearly. When you copy the final prompt over, you can keep or strip the formatting, they’re there to make planning easier for you.
Q: How does token estimation help?
It’s your safety net before you hit send. Check your prompt size upfront so you can trim context or split oversized requests into smaller chunks. This prevents the dreaded ‘token limit exceeded’ mid-workflow and helps you budget your AI calls throughout the day.
Q: Can I use keyboard shortcuts to speed things up?
The tool has standard keyboard navigation for moving around. But honestly, the real speed boost is opening multiple browser tabs with different prompts running in parallel, this way you’re never bottlenecked waiting on one prompt, and momentum just keeps flowing.
Q: Should I review AI output while it’s still generating?
Absolutely. While one instance is churning out code or content, jump into another window and review what came back from the previous run, spot-check diffs, test deliverables, hunt for bugs. Catching issues early in parallel beats waiting for everything to finish and then discovering problems.
I built a tool for the weirdest bottleneck in AI-first development: waiting
by u/Crazy-Treat5901 in PromptEngineering