Lumi Docs
  • About Lumi AI
  • Setting Up
    • Quick Start
    • 1. Connect
    • 2. Configure
      • Configuration Tips
      • Add Tables & Fields
      • Add Business Context
      • Advanced Configuration
    • 3. Distribute
  • Using Lumi
    • Getting Started
    • Lumi Use Cases
      • Data Exploration
      • Business Metrics
      • Anomaly Detection
      • Trend Analysis
      • Root Cause Analysis
      • Data Quality
    • Best Practices
      • Prompting Best Practices
      • Boards Best Practices
      • Versioning Best Practices
    • Chat Limitations
    • Knowledge Base Utility
      • Curated Prompts
      • Leveraging Memories
    • Network Configuration
  • Product Features
    • Chat
    • Boards
    • Knowledge Base
      • Overview
      • Connection
      • Tables
        • Custom Fields
      • Model
      • Business Context
      • Memories
      • Users
      • Restoration
    • Organization Settings
      • Organization Profile
      • Tool Integrations
      • Gateway Management
      • Members
    • User Profile
    • Notifications
    • Data Gateway
      • Deployment
      • Configuring for Boot
    • Source System Integrations
      • PostgreSQL
      • Microsoft SQL Server
      • MySQL
      • Databricks
      • BigQuery
      • Snowflake
      • SAP HANA
      • Oracle
      • AWS Athena (Pre-release)
    • Secondary Interfaces
      • Slack
      • Microsoft Teams
    • Release Notes
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  1. Using Lumi

Limitations

Lumi AI’s performance depends on the clarity and structure of input data. Ambiguous or overly broad queries may lead to less precise results. For highly detailed analyses, structured queries and well-defined context yield better insights. Below are a few areas to be aware of to gain the best results.

Last updated 3 months ago

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