Close the feedback loop
on LLM prompts
Capture feedback. Detect issues. Generate better prompts — automatically.
Your prompts are failing
and you don't know it
Bad outputs go unnoticed. Feedback lands in support tickets. Prompt fixes are guesswork.
- No visibility into bad outputs
- Ad-hoc, unversioned prompt changes
- Manual rewrites for every improvement
“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 user tells support “your app gave me a wrong answer”. That's it. Five handoffs later, engineering is staring at a Jira ticket trying to guess what went wrong. Sounds familiar?
- No context of problematic prompt or output generated
- Multiple handoffs before it reaches engineering
- Days or weeks from complaint to prompt fix
Features
Collect feedback
Drop in the embeddable widget or use the SDK and collect feedback in minutes. See the feedback alongside the exact prompt payload to get a clear picture.
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Detect issues
Cobbl automatically detects issues from feedback, allowing you to quickly and clearly identify areas of concern in your prompts. See patterns, not noise.
Inaccurate responses
Users report receiving factually incorrect information in responses.
Make real improvements
Cobbl recommends improvements to your prompts based on user feedback. You review, edit, approve – keeping prompts optimized.
Inaccurate responses
Users report receiving factually incorrect information in responses.
Add explicit source-citation instructions and a fact-checking step to the prompt template.
Seamless versioning
Manage prompt versions and view version feedback and analytics. Issue with your prompt? Instantly rollback to a previous version.
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.
Three steps to better prompts
Collect feedback in under 5 minutes.
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', }) // Store the runId for feedback collection 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
Manage your prompts, review issues and feedback to improve your prompts over time, view how people use your prompts with prompt runs, and review prompt analytics, all in the Cobbl dashboard.
Built for engineering teams
Everything you need for prompts at scale.
Multi-Provider
Instant integration with OpenAI, Anthropic and Google.
Environment Isolation
Create environments according to your app's needs.
Role-Based Access
Engineers build, reviewers triage feedback.
Analytics
Tokens, latency, success rates, and feedback trends.
Type-Safe SDK
Fully typed SDK built on TypeScript.
Lightweight Widget
Under 15 KB. React, vanilla JS, and script tag.