Prompt Library

Explore a collection of useful prompts for getting the most out of nao. Use these examples to accelerate your workflow and discover new ways to interact with your data.

dbt

Create a dbt model

Create a dbt model called {model_name} that {description}, as well as its yaml documentation.
{Start from existing dbt models {ref_names}}
{Final output should include these columns: {column list}}
{Create this model in the {layer_name} layer / {folder_name} folder}

dbt

Create staging dbt model from table

table

Create a staging model from this table, as well as its yaml documentation.

dbt

Convert SQL to dbt model

file.sql

Create a dbt model called {model_name} from this SQL code, as well as its yaml documentation.
{Create this model in the {layer_name} layer / {folder_name} folder}

dbt

Explain a dbt model

dbt_model.sql

Explain what this dbt model does, including:
- Business purpose and use case
- Data sources and transformations
- Key metrics and calculations
- Downstream usage

dbt

Improve a dbt model

dbt_model.sql

Review this dbt model and suggest improvements for:
- Performance optimization
- Code readability and maintainability
- dbt best practices compliance
- repository best practices compliance

dbt

Write documentation of a model

dbt_model.sql

Add yaml documentation for this dbt model, following these rules:
- Add documentation in the relevant yaml file if it already exists, otherwise create it in the relevant folder. 
- Compare dbt model schema and yaml documentation, so that documentation is up to date with dbt model
- Follow existing yaml best practices of repository. 
- Reuse existing documentation already written for some columns.

dbt

Write tests for a model

dbt_model.sql

Add dbt tests for this dbt model, following these rules:
- Add basic tests in the relevant yaml file if it already exists, otherwise create it in the relevant folder. 
- Add unique and not null tests for key/relevant columns only
- Follow existing yaml tests best practices of repository.

dbt

Write custom dbt test

dbt_model.sql

Create a custom dbt test for this dbt model that validates {business_rule}.
The test should render all the lines where {conditions when the test fails}

dbt

Write dbt unit tests

dbt_model.sql

Generate unit tests for this dbt model using dbt's unit testing framework.
Include test scenarios for {test_scenarios} with mock input data and expected outputs.

dbt

Get dependencies of a dbt model

dbt_model.sql

Show me all the upstream and downstream dependencies for this dbt model.
Include both direct and indirect dependencies with their relationship types.

dbt

Get dependencies of a column in dbt lineage

Find all models that use the column {column_name}.
Show me the complete lineage and how this column flows through the data pipeline.

dbt

Rename a column through the lineage

dbt_model.sql

Rename the column {column_name} to {new_column_name} in {this model / all models}
Identify all downstream models that will be impacted and provide the necessary changes.
Update all related documentation and tests accordingly.

dbt

Remove a column through the lineage

dbt_model.sql

Remove the column {column_name} from {this model / all models}
Identify all downstream models that will be impacted and provide the necessary changes.
Update all related documentation and tests accordingly.

dbt

Add a column in final layer

dbt_model.sql

Add a new column {column_name} int his model.
If column is not directly available in this model, add the necessary ref to the model to make it available. Make sure you follow the layers rules in doing so.
Update all related documentation and tests accordingly.

dbt

Turning a model into incremental

dbt_model.sql

Convert this model to use incremental materialization.
Set up appropriate unique keys, incremental strategy, and handle late-arriving data.

dbt

Optimize data model

Audit dbt project for better consistency and scalability:
- Detect code logics that can be factorized
- Detect dbt models that can be broken into smaller logics
- Detect dbt models that are not used 
- Detect inconsistencies in fields / metrics definitions
- Detect inconsistencies in the different data layers rules

dbt

Rename a dbt model

dbt_model.sql

Rename this model to {new_model_name}.
Update all references, dependencies, and documentation throughout the project.

SQL

Write SQL query

Write a SQL query that {description}.
{Start from these tables: {table_names}}
{Final output should include these columns: {column list}}
{Create this SQL query in {folder_name} folder / an SQL worksheet}

SQL

Improve an SQL query

file.sql

Review this query and suggest improvements for:
- Performance optimization
- Code readability and maintainability
- repository best practices compliance

SQL

Explain an SQL query

file.sql

Explain what this SQL query does, including:
- Business purpose and use case
- Data sources and transformations
- Key metrics and calculations

SQL

Fix an SQL error

file.sql

Fix this SQL error:
Error: {error_message}

Data exploration

What are the values for a column?

table

What are the distinct values in the column {column_name}?

Data exploration

What's the value distribution for a column?

table

Analyze the distribution of values in {column_name} from this table.

Data exploration

What's the granularity of a table?

table

Determine the granularity of this table.
Identify the primary key and explain what each row represents in business terms.

Data exploration

Json keys extraction in a column

table

Extract the JSON keys from the column {json_column} in this table.
Output the list of all the keys.

Data exploration

Find relevant table for an analysis

What are the most relevant tables and columns in the data warehouse to analyze: {analysis to run / information to find}

Data exploration

Run analytics on your data

Run this analysis: {analysis description}
Write SQL query, and execute it to answer the question.

Data quality checks

Are there null values in a column?

table

What's the percentage of null values in this column: {column_name}?

Data quality checks

Are there outlier values in a column?

table

Detect any outlier values in this column: {column_name}

Data quality checks

Data diff between dev and production

Profile these 2 tables of dev and production and compare them.
Provide a summary of differences in number of rows, schema, value distribution.

Git

Write PR description

Generate a pull request description for the changes made.
Include:
- Business description of changes
- Summary of code changes
- Summary of data output changes

Data service desk

Write full PR to answer to a business user request

Create all necessary code changes for this business request:
{business_request}
Including models, tests, documentation, and PR description.
Make sure you check the code changes don't break any dependencies in the repository. Run any data quality checks needed to validate the code changes.

Data service desk

Run full analysis to answer a business user request

Run full analysis to answer this business request:
{business_request}
Find the relevant tables/columns to answer the request, and write the corresponding queries.
Answer with answer in plain english + raw query results to illustrate answer.

Pandas

Write pandas code to clean data

table

Write python code to import this data as a pandas dataframe.
Prepare the dataset with the following transformations:
- Filter data on these rules: {filtering rules}
- Create these new columns: {columns to create}

Machine Learning

Write python code for feature engineering

table

We want to create a ML model for {describe ML model}
Write feature engineering Python code to prepare the dataset for this ML model:
- Import this data as a pandas dataframe
- Filter data on these rules: {filtering rules}
- Create these new columns: {columns to create}
- Generate features relevant for the model

Machine Learning

Write python code to create forecasting model

table

We want to create a ML model for forcasting {describe forecasting target}
Write Python code to:
- Import this data as a pandas dataframe
- Prepare the dataset for this ML model
- Write the training and test framework
- Implement ML model
- Measure performance of ML model

Machine Learning

Write python code to create scoring model

table

We want to create a ML model for scoring {describe scoring target}
Write Python code to:
- Import this data as a pandas dataframe
- Prepare the dataset for this ML model
- Write the training and test framework
- Implement ML model
- Measure performance of ML model

dbt​

SQL​

Data exploration​

Data quality checks​

Git​

Data service desk​

Pandas​

Machine Learning​

dbt

SQL

Data exploration

Data quality checks

Git

Data service desk

Pandas

Machine Learning