Karini AI Documentation
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  • Introduction
  • Installation
  • Getting Started
  • Organization
  • User Management
    • User Invitations
    • Role Management
  • Model Hub
    • Embeddings Models
    • Large Language Models (LLMs)
  • Prompt Management
    • Prompt Templates
    • Create Prompt
    • Test Prompt
      • Test & Compare
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      • Create Agent Prompt
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  • Datasets
  • Recipes
    • QnA Recipe
      • Data Storage Connectors
      • Connector Credential Setup
      • Vector Stores
      • Create Recipe
      • Run Recipe
      • Test Recipe
      • Evaluate Recipe
      • Export Recipe
      • Recipe Runs
      • Recipe Actions
    • Agent Recipe
      • Agent Recipe Configuration
      • Set up Agentic Recipe
      • Test Agentic Recipe
      • Agentic Evaluation
    • Databricks Recipe
  • Copilots
  • Observability
  • Dashboard Overview
    • Statistical Overview
    • Cost & Usage Summary
      • Spend by LLM Endpoint
      • Spend by Generative AI Application
    • Model Endpoints & Datasets Distribution
    • Dataset Dashboard
    • Copilot Dashboard
    • Model Endpoints Dashboard
  • Catalog Schemas
    • Connectors
    • Catalog Schema Import and Publication Process
  • Prompt Optimization Experiments
    • Set up and execute experiment
    • Optimization Insights
  • Generative AI Workshop
    • Agentic RAG
    • Intelligent Document Processing
    • Generative BI Agentic Assistant
  • Release Notes
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  1. Prompt Management

Test Prompt

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Last updated 11 months ago

Karini AI's prompt playground offers tooling for extensive prompt testing including A/B testing against different models, ability to view live traces and maintain audit trail of all prompt experiments with an option to save prompt runs.

Karini AI’s prompt playground revolutionizes how prompts are created, tested, and perfected across their lifecycle. This user-friendly and dynamic platform transforms domain experts into skilled prompt masters, offering a guided experience with ready-to-use templates for kickstarting the prompt creation. Users can quickly evaluate their prompts using different models and model parameters focusing on response quality, number of tokens, and response time to select the best option.

Test and Compare prompts
Observability/View traces
Save Prompt Runs