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# Generate with AI

> Describe a dataset in plain language and Mammoth generates up to 500 rows of realistic test data for pipelines, dashboards, and training.

Generate with AI creates a dataset from a plain-language description. Use it when you need test data and do not have a real source yet. Describe the data you need and preview the result in the same window. The **Generate with AI** option appears in the New Dataset dialog only when the feature is enabled for your workspace.

![Generate with AI dialog with a prompt box, Let AI decide or User defined row count options, and a Generate button](https://docs.mammoth.io/api/v1/images/20260324_111631_AIDatasetGenerationinterface.jpg)

Use it for testing transformation pipelines, prototyping dashboards, training team members, and creating documentation examples without exposing sensitive data.

## When to use Generate with AI

### Pipeline testing

Build and test your transformation logic before real data becomes available. Create sample data that matches your expected schema, then develop your entire pipeline workflow.

**Scenario**: Your IT team is still working on database access, but you need to start building monthly sales reports.

**Solution**: Generate sales data with the columns you expect, build your pipeline, then swap in real data when ready.

### Dashboard prototyping

Create visualization layouts and get stakeholder approval without waiting for data access or dealing with compliance concerns.

**Scenario**: Board presentation in 2 weeks, but data warehouse project won't complete for a month.

**Solution**: Generate executive metrics data, build the dashboard layout, get approval on design and KPIs.

### Team training

Practice using Mammoth's features with realistic data that won't trigger compliance or privacy concerns.

**Scenario**: New analysts need to learn the platform but can't access customer data during training.

**Solution**: Generate customer transaction data for hands-on practice with all platform features.

### Documentation and demos

Create consistent examples for tutorials, user guides, and sales demonstrations.

**Scenario**: Building training materials that need to work across different customer scenarios.

**Solution**: Generate industry-specific datasets that showcase relevant use cases.

![Diagram with Generate with AI at the center linked to four uses: pipeline testing, dashboards, training, and demos](https://docs.mammoth.io/api/v1/images/20260324_111122_Usecasediagramshowingfourbranches_.jpg)

## How Generate with AI works

### Steps to generate a dataset

1. **Start Creation**: Click **+ New Dataset** → **Generate with AI**
2. **Write Prompt**: Describe your desired dataset in natural language
3. **Set Row Count**: **User defined** is selected by default with 30 rows. Enter an exact number of rows (1 to 500), or choose **Let AI decide**
4. **Generate Dataset**: Click **Generate** and wait for the preview to load.
5. **Review Preview**: Validate the suggested dataset name (you can edit it), column structure, and sample records. The preview is read-only. Select the **X** button in the preview bar to discard it.
6. **Finalize**: Click **Create new dataset** to proceed, or, to adjust the result, click the collapsed prompt to edit it (this discards the preview), then click **Generate** again.

The new dataset appears in the Data Library.

[Video: Watch on YouTube](https://www.youtube.com/watch?v=VayxSP132zI)

### What happens behind the scenes

The AI analyzes your prompt to determine:

- **Column names and data types**
- **Realistic value distributions** (proper date ranges, sensible price ranges)
- **Data relationships** (defects correlate with temperature, sales peak during holidays)
- **Industry patterns** (financial quarters, manufacturing shifts, healthcare appointment types)

## Writing effective prompts

### Manufacturing prompt example

**Prompt**:

```plaintext
Generate production line data with timestamp, line ID, units produced, 
defects found, operator name, and temperature readings for 8-hour shifts
```

**Generated Result**:

- 30 rows
- Temperature readings: Average, Minimum, and Maximum values per shift (°C)
- Shift Duration: 8 hours per record
- Operators: Realistic names rotating across records
- Temperature: 20–26°C with natural variation across shifts
- Structured production summary at shift level

![Generated production line dataset preview with 7 columns and 30 rows of shift data](https://docs.mammoth.io/api/v1/images/20260324_111502_Manufacturingprompt.jpg)

### Financial services prompt example

**Prompt**:

```plaintext
Create monthly revenue data by product category and region for 2024, 
including columns for month, category, region, revenue, and growth rate
```

**Generated Result**:

- 120 rows (AI-generated sample size)
- Columns: Month, Category, Region, Revenue, Growth Rate
- Monthly records for 2024 in YYYY-MM format
- Revenue: Realistic numeric values with decimal precision
- Growth Rate: Decimal values (e.g., -0.05 to 0.05) showing positive and negative variation
- Categories and regions generated dynamically based on prompt context

![Generated 2024 monthly revenue dataset preview with 5 columns and 120 rows](https://docs.mammoth.io/api/v1/images/20260324_111519_Financialrevenueprompt.jpg)

### Healthcare prompt example

**Prompt**:

```plaintext
Generate patient appointment data including appointment ID, patient demographics, 
appointment type, provider name, insurance type, and appointment date/time
```

**Generated Result**:

- Appointment IDs: Sequential format (APT-2024-0001)
- Demographics: Realistic age distribution, gender balance
- Types: Physical, Specialist, Lab Work, Follow-up
- Providers: Dr. names with appropriate specialties
- Insurance: Medicare, Blue Cross, Aetna, Self-Pay
- Scheduling: Business hours, realistic appointment intervals

### E-commerce prompt example

**Prompt**:

```plaintext
Create customer transaction data with order ID, customer name, product, 
quantity, price, order date, and shipping address for online retail
```

**Generated Result**:

- Order IDs: ORD-20240001 format
- Customers: Realistic names (no actual people)
- Products: Electronics, clothing, home goods with appropriate pricing
- Quantities: 1-5 items typical for consumer orders
- Prices: $15-$300 with product category correlation
- Dates: Recent months with weekend/holiday patterns
- Addresses: Realistic US addresses (no real locations)

## Prompt best practices

### Be specific about columns

**Vague**: "Generate sales data"

**Specific**: "Generate sales data with date, salesperson, product, quantity, unit price, total amount, and customer region"

### Include data types and constraints

**Generic**: "Include dates and amounts"

**Detailed**: "Include order dates from January-March 2024, dollar amounts between $100-$5000"

### Mention relationships

**Independent**: "Add temperature and defect columns"

**Correlated**: "Add temperature readings and defect counts, where defects increase when temperature exceeds 80°F"

### Specify business rules

**No context**: "Include employee data"

**Business logic**: "Include employee data with ID, department, hire date, salary - where IT department salaries are 20% higher"

> **Tip:** Start with a broad prompt to get the basic structure, then regenerate with more specific constraints.

## What Generate with AI can and cannot do

### What AI does well

**Schema Intelligence**: Determines appropriate column names, data types, and relationships from natural language descriptions.

**Realistic Distributions**: Creates sensible value ranges, proper date sequences, and industry-appropriate patterns.

**Domain Knowledge**: Understands business contexts like manufacturing shifts, financial quarters, healthcare workflows, and retail seasonality.

**Data Relationships**: Generates correlated values (sales peak in December, defects increase with temperature, appointment types match provider specialties).

### Current limitations

**Row Count Constraints**: With **User defined**, the maximum is 500 rows per generation (see [limits and quotas](https://docs.mammoth.io/learn/limits-and-quotas)), which suits testing, not analysis

**Generic Patterns**: Creates realistic but not customer-specific distributions (good for prototypes, not exact replicas)

**Static Generation**: No real-time refresh capability (regenerate manually if dataset needs updates)

**Schema Approximation**: Matches general patterns but won't perfectly replicate your production schema structure

![Two-column diagram of what Generate with AI does well and its limits, including a 500 row maximum](https://docs.mammoth.io/api/v1/images/20260324_111208_CapabilitiesvsLimitations.jpg)

## Iterative refinement process

### First generation review

After initial generation, check:

- Do column names match your expectations?
- Are data types appropriate (dates as dates, numbers as numbers)?
- Do value ranges make business sense?
- Are there realistic relationships between columns?

### Refinement strategies

**More Specific Constraints**:

```plaintext
Original: "Generate customer data"
Refined: "Generate customer data with ages 18-75, email addresses, 
phone numbers, and purchase amounts between $25-$500"
```

**Add Business Logic**:

```plaintext
Original: "Include sales rep and revenue"
Refined: "Include sales rep and revenue, where senior reps 
(5+ years experience) have 30% higher average deals"
```

**Include Edge Cases**:

```plaintext
Original: "Generate order data"
Refined: "Generate order data including some cancelled orders (10%), 
partial refunds, and missing shipping addresses for pickup orders"
```

### When to regenerate vs. accept

**Regenerate if**:

- Column names don't match expected schema
- Value ranges seem unrealistic for your industry
- Missing important relationships between data points
- Need more specific business logic

**Accept if**:

- Schema structure is close enough for testing
- Value ranges are reasonable approximations
- Data relationships make basic business sense
- Goal is pipeline testing, not data analysis

## Replace generated data with real data

### The replace pattern

AI-generated datasets serve as placeholders in a "generate → build → replace" workflow:

1. **Generate** sample data matching expected real data schema
2. **Build** complete transformation pipeline using sample data
3. **Test** all transformation logic and business rules
4. **Validate** output format and dashboard compatibility
5. **Replace** with a connector or file upload when real data is available

![Four-step workflow diagram: generate sample data, build the pipeline, test, then replace with real data](https://docs.mammoth.io/api/v1/images/20260324_111254_Workflowdiagramshowing.jpg)

### Schema consistency tips

**Column Names**: Use exact names you expect in production data

```plaintext
Good prompt: "Include columns named customer_id, order_date, 
product_sku, quantity, unit_price"
```

**Data Types**: Be explicit about formats

```plaintext
Good prompt: "Use YYYY-MM-DD date format, decimal prices 
with 2 decimal places, integer quantities"
```

**Relationships**: Mirror expected production relationships

```plaintext
Good prompt: "Each customer should have 1-5 orders, 
each order should have 1-3 line items"
```

## Troubleshoot Generate with AI

### Generated data doesn't match expectations

**Problem**: Column names or data types aren't what you expected

**Solution**: Be more explicit in your next prompt about exact column names and data formats

### Values seem unrealistic

**Problem**: Prices too high/low, dates in wrong ranges, unrealistic patterns

**Solution**: Add specific constraints: "prices between $X-$Y", "dates in 2024 only", "include seasonal patterns"

### Missing relationships

**Problem**: Data columns should be related but appear independent

**Solution**: Explicitly describe relationships: "higher prices should correlate with luxury categories"

### Need more complex schema

**Problem**: Simple generation doesn't match your complex production schema

**Solution**: Focus on core columns needed for testing, add complexity gradually through refinement

> **Tip:** Use generated data as a starting point, not a final solution. Generate the basic structure first, then enhance it with real data patterns as they become available.

## Next steps

Once you have your AI-generated dataset:

1. **Explore the data** using Explore Cards to understand patterns
2. **Build transformation pipelines** to practice your logic
3. **Create prototype dashboards** for stakeholder feedback
4. **Document your workflows** using the sample data as examples
5. **Plan real data integration** to replace sample data when ready

![Five-step next steps diagram from exploring the generated data to planning real data integration](https://docs.mammoth.io/api/v1/images/20260324_111324_Nextstepsworkflow.jpg)

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Source: https://docs.mammoth.io/learn/data-sources/generate-with-ai.md · Updated: 2026-10-13