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Overview

Rowbase includes a powerful AI assistant that helps you work with data through natural language. Chat with your data, get intelligent suggestions, and use AI-powered operations.

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AI Chat

Talk to your data using natural language. The AI assistant understands your datasets and can help you:
  • Explore and understand your data
  • Create transformations using plain English
  • Debug data quality issues
  • Get recommendations for cleaning and organizing

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Starting a Conversation

  1. Open a dataset
  2. Click the Chat icon
  3. Ask a question or describe what you want to do

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Example Prompts

Understanding data:
  • “What columns does this dataset have?”
  • “Show me rows where status is empty”
  • “What’s the distribution of values in the category column?”
Creating transformations:
  • “Convert all email addresses to lowercase”
  • “Parse the date column from MM/DD/YYYY format”
  • “Create a new column that combines first and last name”
Data quality:
  • “Find duplicate rows based on email”
  • “Which rows have invalid phone numbers?”
  • “Flag rows where quantity is negative”

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AI-Suggested Operations

When you import data, the AI analyzes it and suggests operations to clean and transform it.

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How Suggestions Work

  1. Import data - Upload a CSV or paste data
  2. Analysis - AI analyzes the data structure and quality
  3. Suggestions - You’ll see suggested operations at the top of the pipeline
  4. Accept or dismiss - Review each suggestion and accept or dismiss

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Types of Suggestions

  • Header detection - Rename columns from detected headers
  • Type parsing - Convert text to numbers, dates, or booleans
  • Normalization - Standardize formats (snake_case, trim whitespace)
  • Quality fixes - Handle empty values, standardize case
You don’t have to accept every suggestion. Review each one and accept only the ones that make sense for your use case.

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AI Operations

Beyond chat and suggestions, Rowbase includes AI-powered operations you can add to your pipeline.

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Use AI

Generate content based on other columns using AI. Use cases:
  • Categorize products from descriptions
  • Extract entities from text
  • Generate summaries
  • Translate content
  • Enrich data with derived fields
Input: Product description
Prompt: "Categorize this product into: Electronics, Clothing, Home, or Other"
Output: AI-generated category

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Detect Headers

When data has messy or missing headers, AI can detect the actual column names from the data. 
Before: _UNNAMED_0, _UNNAMED_1, _UNNAMED_2
After: customer_name, email, signup_date

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Best Practices

The more specific your request, the better the result. Instead of “fix the dates”, try “parse dates from DD/MM/YYYY format to ISO format”.
AI suggestions are helpful but not always perfect. Always review the preview before accepting.
Chat is great for understanding your data before deciding on transformations. Ask questions first, then build your pipeline.
AI is powerful for complex tasks, but sometimes a simple filter or rename is all you need. Use the right tool for the job.