A note from the developer
Why I built Quill Terminal—honestly
I love working in the terminal. I just stopped accepting that some of my most important AI instructions had to be written like throwaway input.
I spend a lot of my day in the terminal because it is fast, direct, and close to the work. Quill started with a mismatch I could not stop noticing: the AI tools in the terminal were becoming more capable, while the place where I prepared their instructions still felt like a one-line input box.
Desktop AI apps, IDEs, and text editors naturally give us room to think. Larger context windows give us room to send more, and AI CLIs have improved too—many support multiline input, history, and richer editing. That is useful. But a more capable input field is still different from a dedicated writing surface. For a quick question, the difference does not matter. For a prompt carrying code, logs, assumptions, requirements, constraints, and a definition of done, it does.
The surface changes how we write. An input field encourages us to get the thought in and press Enter. An editor invites us to slow down, step back, move a requirement, fence a log, fix a typo, notice an unstated assumption, and reread the whole thing before it starts an agent run.
Try this with your last serious prompt
Paste it into an empty editor. Then zoom out.
Read it as a document instead of a command. Natural-language instructions may run into assumptions. Requirements hide inside prose. Code and logs blur into the task. A constraint appears once and disappears in the middle. The prompt may have worked, but the editor makes its ambiguity visible.
The editor did not improve the idea for you. It gave you the conditions to improve it yourself.
That is why I built Quill Terminal. Not because every prompt needs headings and Markdown, but because terminal-first developers—and teams working through GenAI gateways—should not have to leave their workflow to get a proper composing surface.
Quill gives the draft room before it becomes a command. Move through it like a document, separate instructions from evidence, correct a typo without searching through shell history, and reread the complete prompt at your own pace. Nothing is rewritten for you, and nothing is handed off until you decide it is ready. I built it because the prompt is where the work happens—especially when that prompt can guide agents, subagents, tool calls, and minutes of execution.
Does structure actually help the model?
Yes, when the prompt actually has parts. A model does not use Markdown like a visual document editor, but headings, lists, and fences are familiar boundaries from code, documentation, and the web. More importantly, those boundaries make your intent explicit: this is context, this is a requirement, this must not change, and this is what success looks like.
Separate copied code, logs, and output from the instructions about them. That boundary helps both you and the model keep evidence distinct from the task.
Give context, requirements, constraints, and the desired output stable places in a prompt instead of letting them blur together.
Put one requirement on each line, making the work easier to count, review, and check off than requirements hidden inside prose.
Useful for human scanning, but not a substitute for explicit instructions or boundaries. Use emphasis as polish, not control flow.
| Element | Signal | The honest read |
|---|---|---|
| ``` code fences | Clear | Separate copied code, logs, and output from the instructions about them. That boundary helps both you and the model keep evidence distinct from the task. |
| ## headings | Clear | Give context, requirements, constraints, and the desired output stable places in a prompt instead of letting them blur together. |
| 1. and - lists | Useful | Put one requirement on each line, making the work easier to count, review, and check off than requirements hidden inside prose. |
| **bold**, *italic* | Light | Useful for human scanning, but not a substitute for explicit instructions or boundaries. Use emphasis as polish, not control flow. |
A one-paragraph question gains little from structure. Once a prompt mixes several kinds of information, structure earns its keep.
The deepest win isn't in the model at all
Writing ## Requirements forces you to actually know your requirements. Writing ## Constraints makes you state them instead of hoping they're implied. The clarity the model responds to is clarity you created by structuring your own thinking — Quill Terminal's editor just makes that the path of least resistance.
What it costs — honestly
Markdown markers add a few characters, so they add some tokens. For a prompt large enough to need structure, that overhead is usually negligible beside the cost of a retry, the wrong file edit, or an agent running with a misunderstood constraint. A well-designed prompt is cheap leverage in an expensive loop. In agentic work, prompts are closer to specs than throwaway lines, so Quill Terminal treats them as documents you draft, refine, save, and reuse.
Saved prompts compound. Blobs evaporate.
A good prompt typed into a chat box is easy to bury in session history. A prompt you designed in Quill Terminal is an asset: saved as a .md file, reusable as a template, findable in History, and shareable as plain text or alongside the project it belongs to. The craft is part writing and part engineering: draft it once, inspect the result, and improve the prompt each time you run it. The effort compounds across projects, models, and teammates instead of disappearing into a transcript.
Design the prompt once. Improve it every time.