Fresh drop from r/PromptEngineering: JigBench, the free browser workbench for building prompts from 10 commands, just shipped two updates. Both came straight from Reddit feedback. The second one is the twist.
What’s new
JigBench lets you assemble instructions from 10 commands, preview the full prompt, and take it to ChatGPT, Claude, or whatever AI tool you prefer. There’s no account and no API key. JigBench itself doesn’t run a model, so nothing you build is sent anywhere until you paste it into a tool of your own choosing. That also means you can use it with any model, today or next year, without touching a setting.
The maker, u/moonshotconsulting, shared it a few weeks ago and got two pieces of feedback worth building around:
- Download prompt (.txt): export the assembled prompt as plain text, next to the existing Copy button. This is handy when you want to attach the prompt to a ticket, drop it into a shared drive, or keep a dated copy of the exact wording you ran.
- Workflow notes: attach optional notes to a named command sequence.
The first is a small convenience. The second is where it gets interesting.
The twist
One commenter pointed out that saving a command sequence isn’t the same as remembering what it worked for. That’s the gap. Most of us have a folder of saved prompts that made sense on the day we wrote them. Three weeks later it’s a pile of files with no memory attached. One commenter described exactly that: a folder full of prompt exports that mean nothing because they can’t remember what each was for. Names like “final_v2” and “better_prompt” don’t help when you open them cold and have to guess what the output was supposed to look like.
So JigBench now has three optional note fields on every saved workflow:
- Used for
- What worked
- What to change next time
There’s no automatic scoring and no result tracking. You run the prompt in your own AI tool, then write down what you saw. The notes are manual on purpose, and the maker says so up front. The idea is that a sentence written by a person who just read the output is worth more than a metric nobody trusts.
Mini-workflow
- 🧩 Build your command sequence in JigBench and name it. Pick a name that describes the job, not the version, like “JD cleanup for recruiters.”
- 📋 Copy the prompt, or download the .txt, and run it in your AI tool of choice. Read the whole output before you judge it, not just the first paragraph.
- 📝 Come back and fill in the three notes: what you used it for, what worked, what to change next time. Do it right away, while the result is still fresh in your head.
A real example from recruiting
The maker works in recruiting. Their example: take a vague job description that says “strong communicator” (which tells candidates almost nothing) and run the Specificity command on it. A good instruction might read:
“Using only the responsibilities supplied, propose observable communication tasks. Flag anything that needs the hiring manager’s input instead of inventing requirements.”
The second sentence is the useful part. It tells the model to flag gaps instead of making things up. After you try it, the “What worked” note is where you record whether that actually held. For example, you might write that the model turned “strong communicator” into tasks like running a weekly client update and writing a handoff summary, and that it flagged two requirements for the hiring manager instead of guessing. Then “What to change next time” might say to add the team size up front, since the model asked about it. Next month, that short record saves you from rebuilding the whole thing from memory.
Pro tips
- Write the “Used for” note first. If you can’t describe the task in one line, the workflow probably isn’t specific enough to reuse.
- Use “What to change next time” as your to-do list. It turns every run into a small iteration instead of a one-off. Open the workflow, read that note, tweak one command, and run it again.
- Keep notes short and concrete. One or two lines that mention what you actually saw beat a vague “worked well” every time.
- Know the limits. Workflows and notes live in that browser only, and clearing browser storage removes them. Your working material, goal, and context aren’t saved with the workflow. If something matters, download the .txt.
Your turn
The maker is asking an honest question: does a small task-and-result note make a saved workflow more useful, or would you still rather keep prompts in a document? They also want examples where this adds work instead of saving it. That’s a good prompt for the comments.
If you want to try it, JigBench is free at https://jigbench.com/ and the original thread is on r/PromptEngineering. Tell them where the notes help and where they get in the way. 🏴☠️
Frequently Asked Questions
Q: Do the notes go stale when the prompt changes?
They can, because JigBench doesn’t update them for you. Rewrite the notes whenever you revise the prompt so they describe the current version. If the old results still matter, copy them into the ‘What worked’ field before you edit.
Q: Why keep notes with the workflow instead of in a document?
One commenter said prompt exports named things like ‘v3_final_real’ stop making sense after a few weeks. Attaching the purpose and results to the command sequence keeps them together, so you don’t have to dig through a scratchpad later. Notes are stored in your browser, though, so clearing browser storage removes them. Keep a copy elsewhere if the notes matter to you.
Q: Does JigBench check whether a workflow actually worked?
No. You run the prompt in ChatGPT, Claude, or another AI tool, then write down what you observed in the notes. JigBench doesn’t score results or track them automatically. Think of the notes as a record of your own experiments, not a testing system.
Feedback here changed JigBench: plain-text exports and notes for remembering which prompt workflows worked
by u/moonshotconsulting in PromptEngineering