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Prompts

Build once. Deploy everywhere.

Version-controlled prompts with variables, structured outputs, and model-agnostic deployment. Build your prompt library once and run it in chat, API, or workflows.

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app.aisle.sh

Customer Feedback Analyzer

v12 ยท Last edited 2 hours ago

System Message
You are a customer feedback analyst. Analyze the provided feedback data and extract actionable insights. Always structure your response with: - Themes with frequency percentages - Overall sentiment breakdown - Recommended next actions
User Message
Analyze the following customer feedback and identify the top themes. Focus on churn risk signals and areas requiring immediate attention. Feedback to analyze: {{feedback_data}}
Recent Activity
Chat[email protected]2 min ago
APIsystem14 min ago
Workflowfeedback-pipeline1 hr ago
MODEL
Claude Sonnet 4.5
CONNECTED TOOLS
Slack
Jira
Add connector
DEPLOY
ChatLive
Workflows3 active
APIEndpoint active
Structured Output
MODEL SETTINGS
Max Tokens20000
Temperature0.4
Thinking

Everything you need to manage prompts at scale

Version control for every prompt

Every edit creates a new version. Diff any two versions side-by-side, see who changed what, and roll back with one click. No more untracked edits in chat threads or shared docs.

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Variables and structured outputs

Define input variables once, enforce JSON output schemas, and use the same prompt across every deployment. Get predictable, schema-validated responses instead of parsing unstructured text.

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Deploy to chat, API, or workflow

One prompt serves your team in chat, your application via API, and your automations in workflows. Change the prompt once and every deployment picks up the update.

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Team sharing and permissions

Control who can edit, run, or view each prompt. Build a shared library your whole team discovers and uses. Stop being the single point of failure for your team's AI tools.

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Prompts are one piece. Here is how the rest of the platform fits together.

Build once, run everywhere.

Version control, team sharing, and multi-model deployment from one platform.

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