Product feedback for Lovable

This page is for anyone building with Lovable who wants to give the chat feedback that survives into the next prompt cycle. The same pattern applies across the feedback for AI coding agents hub.

The problem with feedback in Lovable workflows

Lovable iterates fast. You describe what you want, it generates a build, you preview, you ask for changes. The hard part is "you ask for changes." If the preview has six things wrong with it, the usual flow is to type a paragraph trying to describe all six, often misnaming components and skipping nuance. Lovable does its best, fixes the things it understood, and the next preview surfaces a different mix of issues. The iteration loop drifts.

What you want is to walk the preview once, capture each issue with the screen visible, narrate the fix in plain English, and hand Lovable a single artifact that names every finding. Then the next chat message can be one line.

The CobaltCapture workflow with Lovable

Open your Lovable preview. In a new tab, hit Capture screen at cobaltcapture.com, pick the Lovable preview window, drag a box around the broken element, hit Dictate and talk through it. "The hero copy is off-center on tablet, the secondary button uses the wrong color from the design system, and the spacing under the form has doubled since the last iteration." Repeat for each issue. Hit Publish.

Back in Lovable's chat:

Here's the design review: https://cobaltcapture.com/r/<slug>

Apply the fixes in order. After each one, summarize what you changed
and which component it landed on before moving to the next.

Lovable follows the URL, pulls the screenshots into its context, and works through the document instead of guessing from your sentence.

Why this works for Lovable

Lovable's input is chat, and chat is a constrained surface. A pasted URL lets you smuggle in a structured payload through that constrained surface, screenshots, captions, source URLs, dictated context, in a single message. The chat stays clean, the iteration loop stays tight, and you do not lose findings to the messiness of paragraph-form feedback.

The dictation step also helps Lovable specifically because Lovable is good at translating natural-language intent into UI changes but bad at inferring intent from a screenshot alone. Your spoken reasoning fills in the gap. "The submit button should use the secondary style because this is a low-stakes flow" produces a different patch than "fix the submit button."

Alternatives and tradeoffs

You could type all six findings into the chat. That works for two. For six, you skip things. Each skipped detail becomes a future iteration.

You could record a Loom and link it in the chat. Lovable cannot watch the video. The narration that would have made it useful to a teammate is invisible to the agent, this is the same tradeoff that makes CobaltCapture a Loom alternative for agent workflows.

You could fix the issues yourself in the underlying code. That defeats the purpose of using Lovable, and most of the iterations are not really about fixing code, they are about steering the model toward a design that matches your intent. CobaltCapture is what makes that steering legible to the model.