· 6 minute read
Speaking Expands, Typing Compresses
Why speaking my prompts helps me explain more of the reasoning behind a request.
A friend at a cafe told me to try voice dictation. He's a senior engineer, critical of tools by nature, so I took it seriously. That afternoon I downloaded Wispr Flow. Within a week I had the pro plan. It's become the most impactful tool I've used in the past year, outside of Claude Code.
I don't use it for Slack messages or emails or writing articles from scratch. For most communication, I still value typing, the deliberate word choice. But for one specific use case, voice has changed everything: prompting in AI conversations.
When I type a prompt, I tend to edit out the tangents. When I speak, I include more of the reasoning behind my request. That extra context has helped in my AI conversations.
The self-editing problem
When you type a prompt, you're doing two things at once: thinking and editing. You start a sentence, reconsider, delete half of it. You tighten as you go. You cut the tangent before it forms. By the time you hit enter, you've given the model the cleaned-up version of your thinking.
Sometimes I edit out details the model would have needed.
Relevant background can help the model understand what I mean. A tangent I would have deleted may explain my intent. More words are not automatically better, though: I still need to remove distractions and correct contradictions.
What voice changes
When you speak, you say things you wouldn't type because typing gives you time to reconsider.
The result is messier. More words, more tangents, more "actually, let me think about this differently." The transcript includes reasoning I might have left out of a typed prompt. AI can help me organize it. It can ask clarifying questions. What it can't do is recover the context you filtered out before you sent it.
More context, more nuance
I noticed this first in coding conversations. When I typed instructions for a feature, I'd give the essentials. When I spoke them, I'd naturally include why I wanted it, what I'd tried before, what I was worried about, how it connected to other parts of the project. The spoken version was three times longer and significantly more useful.
button"
The nuance matters. "Add a button that does X" is different from "I've been thinking about adding a button that does X, because users keep asking about it in the feedback, but I'm not sure where it should go because the settings screen is already crowded, and I don't want to add another tab, so maybe it could live in the menu bar dropdown instead?" The second version gives the model everything it needs to make a good suggestion. The first makes it guess.
Unexpected directions
Speaking leads somewhere typing doesn't. When I talk through a problem, I often land in a different place than where I started. Explaining it out loud helps me notice connections I hadn't made before. I'll start explaining what I want to build and realize halfway through that I'm describing something slightly different. Better, usually.
This doesn't happen when I type. Typing is too slow. I have time to stay on the original track. Speaking lets me follow a new thought before I edit it away.
For creative projects, this is invaluable. For coding projects, too. Some of my best architectural decisions came from voice-explaining a problem and noticing, mid-sentence, that I was solving the wrong thing.
The brain dump workflow
Voice dictation works especially well for longer inputs. I recently walked my dog for 30 minutes, talking through everything I knew about a topic. No structure, no editing, just a stream of thoughts captured by dictation. That brain dump became a prompt. I brought the transcript to an AI conversation, and we turned it into the essay you're reading now.
The raw transcript was repetitive with half-finished thoughts. But it was also rich. Real experience, specific memories, genuine insights. Wispr Flow cleans up the filler words automatically, so what I got was my actual thinking without the "um"s and "you know"s. That cleanup matters. I wouldn't be as confident sharing raw speech otherwise. We went back and forth a few times, and an hour later, the essay was done.
I couldn't have produced this essay so fast by sitting down and typing a prompt. I would have edited as I typed and left out some of the details that made the transcript useful.
When to type, when to speak
This isn't about replacing typing entirely. Some prompts are better typed. Quick commands. Precise technical instructions. Anything where you know exactly what you want and just need to say it clearly.
But for anything exploratory in an AI conversation, anything where you're still figuring out what you think, anything where context and nuance matter: speak it. Give the model your actual thinking, not the compressed version.
Known solutions
Quick edits
Code snippets
Explaining context
Brain dumping
Thinking out loud
The bottleneck moved
For a long time, I thought the bottleneck in AI collaboration was the AI. Get a better model, write better prompts, and learn the tricks. But part of the difficulty was my own input. It was how much of my actual thinking I could get into the prompt in the first place.
Dictation makes it easier to explain what I'm thinking. I still need to bring useful experience and a question worth working through.
Parallel conversations
As I've leaned into this workflow, something unexpected emerged. I now work with multiple Claude Code terminals open simultaneously, one for each part of a project, or different projects entirely. I talk to each in turn. Give context to one, check on progress in another, think through a problem in a third.
The indie-hacker corners of the internet are full of people spinning up dozens of agents, orchestrating AI swarms, pushing the limits of parallel execution. I'm not doing anything that ambitious. But I've noticed my own behavior shifting. Where I once worked in a single conversation, I now naturally spread across several.
My wife hears me from the other room. She says it sounds like I'm on calls with colleagues, collaborating all morning. A strange sci-fi moment when you notice it. But the workflow feels human in a way I didn't expect. I'm not staring at a screen in silence. I'm talking through problems, explaining what I want, thinking out loud.
The productivity follows. An hour of speaking across multiple agents gets an immense amount done. Typing to three conversations at once would be exhausting. Speaking to them is just talking.
Dictation made those parallel conversations easier for me to manage.
Late to the party
I'm not discovering something new here. Colleagues who use ChatGPT have been using voice mode for this exact reason. Engineers I know have been dictating into their IDEs for years. I was slow to try it. But now that I have, I'm fully on board for this specific use case.
While I use Wispr Flow, any good voice dictation works. What helped me was speaking before editing. I gave the model details I would otherwise have left out.
Try it for your next complex prompt. Don't type it. Say it out loud, capture it, send the whole thing. See what the model does with the expanded version of your thinking instead of the compressed one.
This is a working document. My workflow keeps evolving as I learn what's possible.