How to Talk Through a Bug With Your AI Agent

When you are stuck on a bug, typing a tidy prompt is the last thing you want to do. Talking it through is faster and more natural. Here is how to explain a bug out loud, turn that explanation into clean text, and hand it to Claude Code or Cursor without breaking your flow.

Why talking beats typing when you are stuck

Debugging is a storytelling problem. Your AI agent works best when it knows the full picture: the goal, the error, the environment, and every dead end you already walked down. Typing all of that is tedious, so most people compress it into a one-line prompt and then wonder why the answer misses. Speaking removes that friction. You can ramble for thirty seconds and cover more ground than three typed sentences, because most people speak around three to four times faster than they type.

There is also a "rubber duck" effect. Saying the problem out loud forces you to sequence your thoughts, and half the time you spot the mistake mid-sentence. Voice dictation captures that same monologue and hands it to the agent as text, so the thinking is not wasted. This is the same reason people are moving to coding by voice on a Mac for more than just comments and commit messages.

The voice debugging workflow

The flow is simple: you speak, the audio is transcribed and cleaned on your Mac, and clean text lands in your agent's input. Because the model runs locally, the loop is fast and private.

What to say: a checklist for a great bug prompt

A good spoken prompt covers the same ground a senior engineer would ask about. Speak these out loud in order and your agent gets everything it needs on the first try. This structure works whether you use Claude Code or Cursor.

• The goal. "I am trying to make the login form redirect to the dashboard on success."
• Expected vs actual. "I expected a redirect, but the page reloads and the URL stays the same."
• The exact error. Read the message, or paste the stack trace and say "here is the error above."
• What you tried. "I already checked the network tab and the request returns 200."
• The constraint. "Do not change the routing library, I want to keep React Router."

How dictation options compare for developers

Not every voice tool fits a coding workflow. The two things that matter most for debugging are whether it types into your agent directly and whether your spoken description of proprietary code stays on your machine.

The gap for developers is private, system-wide dictation that also cleans up speech and knows your function names. A custom dictionary is what keeps terms like useEffect, Postgres or your own module names from being mangled into everyday words. You can also add custom AI prompts so the cleanup always formats your dictation as a structured bug report. The same setup helps when you talk to ChatGPT with voice on your Mac or dictate long notes into apps like Bear.

Keeping your code talk private

Describing a bug often means saying things you should not send to a random server: internal API names, customer data in a log line, an unreleased feature. This is where the processing model matters. With BlaBlaType, both speech recognition and AI cleanup run on your Mac, so your spoken description and its transcript never leave the device. Only the finished text you choose to paste reaches your agent, exactly as if you had typed it. If privacy and offline use are priorities, the plans page lays out what each tier includes.

For teams under an NDA or in regulated work, that on-device boundary is the difference between a convenient tool and one you are actually allowed to use. It also means the workflow keeps working on a plane or a spotty connection, since nothing depends on a cloud call.