Ship AI features
your users actually trust
Cobbl collects end-user feedback on AI outputs and turns it into prompt improvements — so you stop guessing what's broken.
Your prompts are failing
and you don't know it
Your AI features look fine from the inside. But your users see every bad answer, and most of them never tell you about it.
- Users churn quietly when AI outputs miss the mark
- Support tickets mention bad AI responses but engineering never sees them
- You're rewriting prompts based on vibes, not data
The kind of output your users quietly flag and never tell you about.
“The capital of Australia is Sydney, known for its iconic Opera House and harbour. It became the capital in 1901 when the federation was established…”
Your feedback loop
isn't serving you
A paying customer tells your support team “your app gave me the wrong answer.” That's where the signal dies. Five handoffs later, engineering is staring at a ticket with no context about which prompt produced what output.
- No context of problematic prompt or output generated
- Multiple handoffs before it reaches engineering
- Days or weeks from complaint to prompt fix
Features
See what your users actually said
Drop in a lightweight widget or use the SDK to collect feedback on any AI output. Every piece of feedback is linked to the exact prompt, inputs, and response that produced it.
Was this helpful?
Stop reading feedback one-by-one
Cobbl groups related feedback into issues automatically, so you see patterns across users instead of triaging individual complaints. Spot the bad prompt before your next churn call.
Inaccurate responses
Users report receiving factually incorrect information in responses.
Ship a better prompt in minutes, not a sprint
Cobbl suggests a new prompt version based on what your users reported. You review the diff, edit if needed, and publish. No guesswork, no waiting for the next sprint.
Inaccurate responses
Users report receiving factually incorrect information in responses.
Add explicit source-citation instructions and a fact-checking step to the prompt template.
Roll back a bad prompt like you would a deploy
Every prompt change is a versioned snapshot with its own feedback and analytics. If something breaks, roll back to the last known-good version in one click.
Initial version
Feedback, baked in
Drop the widget next to any prompt output. Users tell you what's working without leaving your app.
The team agreed to push the API v2 migration to March to give QA another sprint. Sarah will own the migration guide and Alex is handling the deprecation notices. Open question: whether to keep backward compat for the /users endpoint through Q3.
Add feedback collection in five minutes
SDK and widget. No evals pipeline, no data warehouse, no ML team required.
Run a prompt
npm install @cobbl-ai/sdk
import { CobblAdminClient } from '@cobbl-ai/sdk' // Setup Cobbl Admin Client const cobbl = new CobblAdminClient({ apiKey: process.env.COBBL_API_KEY, }) // Run a prompt const result = await cobbl.runPrompt('welcome_email', { customerName: 'Jane', plan: 'Pro', }) // If storing prompt output, store the runId as well await db.create({ output: result.output, runId: result.runId, })
Create prompts in the dashboard, run them via SDK.
Collect feedback
npm install @cobbl-ai/feedback-widget
import { FeedbackWidget } from '@cobbl-ai/feedback-widget/react' // Drop the widget next to any AI-generated content <FeedbackWidget runId={dbItem.runId} variant="thumbs" />
Don't want a widget? Collect feedback with the SDK →
Manage your prompts in the dashboard
See which prompts are causing problems, review the actual outputs your users flagged, and ship improved versions without a deploy.
Built for small teams shipping AI features
Everything you need to manage prompts without a dedicated ML team.
Lightweight Widget
Under 15 KB. Drop it into React, vanilla JS, or a script tag without slowing down your app.
Type-Safe SDK
Fully typed TypeScript SDK so your IDE catches mistakes before your users do.
Analytics
Track tokens, latency, success rates, and feedback trends per prompt version.
Multi-Provider
Swap between OpenAI, Anthropic, and Google without rewriting your prompt layer.
Environment Isolation
Run staging prompts against staging data so you can test changes before they hit production.
Role-Based Access
Engineers build prompts, reviewers triage feedback. Everyone sees only what they need.
Find out what your users actually think
Free to get started. Set up in minutes.