> ## Documentation Index > Fetch the complete documentation index at: https://docs.getnao.io/llms.txt > Use this file to discover all available pages before exploring further. # 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. 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 **Context:** None\ **Template:** ``` 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} ``` ### Create staging dbt model from table **Context:** Data warehouse table\ **Template:** ``` Create a staging model from this table, as well as its yaml documentation. ``` ### Convert SQL to dbt model **Context:** SQL file\ **Template:** ``` 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} ``` ### Explain a dbt model **Context:** dbt model SQL file\ **Template:** ``` Explain what this dbt model does, including: - Business purpose and use case - Data sources and transformations - Key metrics and calculations - Downstream usage ``` ### Improve a dbt model **Context:** dbt model SQL file\ **Template:** ``` Review this dbt model and suggest improvements for: - Performance optimization - Code readability and maintainability - dbt best practices compliance - repository best practices compliance ``` ### Write documentation of a model **Context:** dbt model SQL file\ **Template:** ``` 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. ``` ### Write tests for a model **Context:** dbt model SQL file\ **Template:** ``` 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. ``` ### Write custom dbt test **Context:** dbt model SQL file\ **Template:** ``` 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} ``` ### Write dbt unit tests **Context:** dbt model SQL file\ **Template:** ``` 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. ``` ### Get dependencies of a dbt model **Context:** dbt model SQL file\ **Template:** ``` Show me all the upstream and downstream dependencies for this dbt model. Include both direct and indirect dependencies with their relationship types. ``` ### Get dependencies of a column in dbt lineage **Context:** None\ **Template:** ``` Find all models that use the column {column_name}. Show me the complete lineage and how this column flows through the data pipeline. ``` ### Rename a column through the lineage **Context:** dbt model SQL file\ **Template:** ``` 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. ``` ### Remove a column through the lineage **Context:** dbt model SQL file\ **Template:** ``` 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. ``` ### Add a column in final layer **Context:** dbt model SQL file\ **Template:** ``` Add a new column {column_name} in this 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. ``` ### Turning a model into incremental **Context:** dbt model SQL file\ **Template:** ``` Convert this model to use incremental materialization. Set up appropriate unique keys, incremental strategy, and handle late-arriving data. ``` ### Optimize data model **Context:** None\ **Template:** ``` 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 ``` ### Rename a dbt model **Context:** dbt model SQL file\ **Template:** ``` Rename this model to {new_model_name}. Update all references, dependencies, and documentation throughout the project. ``` ## SQL ### Write SQL query **Context:** None\ **Template:** ``` 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} ``` ### Improve an SQL query **Context:** SQL file\ **Template:** ``` Review this query and suggest improvements for: - Performance optimization - Code readability and maintainability - repository best practices compliance ``` ### Explain an SQL query **Context:** SQL file\ **Template:** ``` Explain what this SQL query does, including: - Business purpose and use case - Data sources and transformations - Key metrics and calculations ``` ### Fix an SQL error **Context:** SQL file\ **Template:** ``` Fix this SQL error: Error: {error_message} ``` ## Data exploration ### What are the values for a column? **Context:** Data warehouse table\ **Template:** ``` What are the distinct values in the column {column_name}? ``` ### What's the value distribution for a column? **Context:** Data warehouse table\ **Template:** ``` Analyze the distribution of values in {column_name} from this table. ``` ### What's the granularity of a table? **Context:** Data warehouse table\ **Template:** ``` Determine the granularity of this table. Identify the primary key and explain what each row represents in business terms. ``` ### Json keys extraction in a column **Context:** Data warehouse table\ **Template:** ``` Extract the JSON keys from the column {json_column} in this table. Output the list of all the keys. ``` ### Find relevant table for an analysis **Context:** None\ **Template:** ``` What are the most relevant tables and columns in the data warehouse to analyze: {analysis to run / information to find} ``` ### Run analytics on your data **Context:** None\ **Template:** ``` 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? **Context:** Data warehouse table\ **Template:** ``` What's the percentage of null values in this column: {column_name}? ``` ### Are there outlier values in a column? **Context:** Data warehouse table\ **Template:** ``` Detect any outlier values in this column: {column_name} ``` ### Data diff between dev and production **Context:** None\ **Template:** ``` 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 **Context:** None\ **Template:** ``` 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 **Context:** None\ **Template:** ``` 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. ``` ### Run full analysis to answer a business user request **Context:** None\ **Template:** ``` 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 **Context:** Data warehouse table\ **Template:** ``` 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 **Context:** Data warehouse table\ **Template:** ``` 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 ``` ### Write python code to create forecasting model **Context:** Data warehouse table\ **Template:** ``` We want to create a ML model for forecasting {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 ``` ### Write python code to create scoring model **Context:** Data warehouse table\ **Template:** ``` 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 ```