KI-Agentenverhalten mit selbst erstelltem Kontext für Looker-Datenquellen steuern

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Auf dieser Seite wird die empfohlene Struktur für das Verfassen effektiver Prompts für Ihre Daten-Agenten für die Conversational Analytics API in Looker beschrieben. Diese Prompts sind Kontext, den Sie als Strings mit dem Parameter system_instruction definieren.

Wichtige Informationen zum erstellten Kontext in Looker

Looker-Entwickler können auch mithilfe von LookML-Parametern Kontext direkt in Looker-Modellen definieren. Ihr Daten-Agent ruft den entsprechenden Kontext aus diesen LookML-Definitionen ab, ohne dass Sie ihn in den Schlüsseldefinitionen Ihrer Systemanweisungen neu definieren müssen.

Data Agents können Kontext aus den folgenden LookML-Parametern ableiten:

• description: Mit diesem LookML-Parameter wird eine Beschreibung nur für Felder definiert, die den Beschreibungen entspricht, die Sie in den Schlüsseln fields und measures definieren können.
• synonyms: Mit diesem LookML-Parameter werden nur Synonyme für Felder definiert, was den Synonymen entspricht, die Sie in den Schlüsseln fields und measures definieren können.
• relationship: Mit diesem LookML-Parameter wird die join-Beziehung zwischen verknüpften Ansichten definiert.

Beispiele für wichtige Komponenten von Systemanweisungen

Die folgenden Abschnitte enthalten Beispiele für wichtige Komponenten von Systemanweisungen in Looker. Dazu gehören die folgenden Schlüssel:

• tables
• fields
• measures
• relationships
• golden_queries
• golden_action_plans
• glossaries
• additional_descriptions

Beschreibungen dieser wichtigen Komponenten finden Sie auf der Dokumentationsseite Agent-Verhalten mit selbst erstelltem Kontext steuern.

Daten mit tables beschreiben

Der folgende YAML-Codeblock zeigt die grundlegende Struktur für den Schlüssel tables für die Tabelle order_items:

- tables:
  - table:
    - name: order_items
    - tags:
      - ecommerce
      - transaction

Häufig verwendete Felder mit fields beschreiben

Im folgenden YAML-Beispielcode werden die wichtigsten Felder wie total_sale_price, total_gross_margin, created_month_name, 30_day_repeat_purchase_rate und count für die Tabelle orders_items beschrieben:

- tables:
  - table:
    - name: order_items
    - synonyms:
        - sales
        - line items
        - items sold
        - orders
    - fields:
      - field:
        - name: total_sale_price
        - description: "The total monetary value from items sold. Corresponds to 'Revenue'."
        - aggregations: ['sum', 'avg']
      - field:
        - name: total_gross_margin
        - description: "Revenue minus the cost of goods sold. Corresponds to 'Profit'."
        - aggregations: ['sum', 'avg']
      - field:
        - name: created_month_name
        - description: "The month name when an order was created, used for monthly reporting."
      - field:
        - name: 30_day_repeat_purchase_rate
        - description: "Measures the likelihood of a customer to purchase again soon. Corresponds to 'Buying Propensity'."
        - aggregations: ['avg']
      - field:
        - name: count
        - description: "A count of order items."
        - aggregations: ['sum']

In diesem Beispiel wurden die Felder synonyms bereits in der zugrunde liegenden LookML definiert. Sie müssen hier also nicht noch einmal definiert werden.

Geschäftliche Messwerte mit measures definieren

Sie können beispielsweise eine Tabellenkalkulation profit als Berechnung des Umsatzes abzüglich der Kosten definieren:

- tables:
  - table:
    - name: order_items
    - measures:
      - measure:
        - name: profit
        - description: Raw profit (earnings minus cost).
        - exp: earnings - cost
        - synonyms: gains

Tabellenverknüpfungen mit relationships definieren

Sie können beispielsweise eine order_items_to_users-Beziehung zwischen der Tabelle order_items des Explorers „Bestellpositionen“ und der Tabelle users so definieren:

- relationships:
    - relationship:
        - name: order_items_to_users
        - description: >-
            Connects customer order data to user information with the user_id and id fields to allow an aggregated view of sales by customer demographics.
        - relationship_type: many-to-one
        - join_type: left
        - left_table: order_items.order_items
        - right_table: order_items.users
        - relationship_columns:
            - left_column: user_id
            - right_column: id

Tipp :Looker-Entwickler können Join-Beziehungen auch mit dem LookML-Parameter relationship definieren. Wenn die Join-Beziehung für zwei Tabellen (Ansichten) in LookML definiert ist, muss sie hier nicht noch einmal definiert werden.

Genauigkeit mit golden_queries verbessern

Sie können beispielsweise Gold-Abfragen für häufige Analysen für die Daten in der Tabelle order_items so definieren:

- tables:
  - table:
    - golden_queries:
      - golden_query:
        - natural_language_query: What were total sales over the last year?
        - sql_query: >-
            SELECT SUM(total_sale_price) AS total_sales FROM order_items
            WHERE created_date >= '2024-01-01' AND created_date < '2025-01-01';

Alternativ können Sie Golden Queries mit einer Looker-Abfrage anstelle einer SQL-Abfrage definieren:

- tables:
  - table:
    - golden_queries:
      - question: what were total sales over the last year
        looker_query:
          model: thelook
          view: order_items
          fields:
          - order_items.total_sale_price
          filters:
            order_items.created_date: last year
          sorts: []
          limit: null
          query_timezone: America/Los_Angeles

Mehrstufige Aufgaben mit golden_action_plans skizzieren

Sie können beispielsweise einen Aktionsplan definieren, um Aufschlüsselungen von Bestellungen nach Altersgruppe zu präsentieren, und Details zur SQL-Abfrage und zu den Schritten für die Visualisierung einfügen:

- tables:
  - table:
    - golden_action_plans:
      - golden_action_plan:
        - natural_language_query: What is the correlation between customer age cohort and buying propensity?
        - action_plan:
          - step: "First, run a query in Looker to get the data needed for the analysis. You need to group by `users.age` (NOT AGE TIER) and calculate the average `order_items.30_day_repeat_purchase_rate` for each age."
          - step: "Then, pass the resulting data table to the Python tool. Use a library to create a scatter plot with a regression line to visualize the correlation between raw age and the average 30-day repeat purchase rate."

Geschäftsbedingungen mit glossaries erläutern

Sie können beispielsweise Begriffe wie allgemeine Unternehmensstatus und „treuer Kunde“ entsprechend Ihrem spezifischen geschäftlichen Kontext so definieren:

- glossaries:
  - glossary:
      - term: Loyal Customer
      - description: A customer who has made more than one purchase. Maps to the dimension 'user_order_facts.repeat_customer' being 'Yes'. High value loyal customers are those with high 'user_order_facts.lifetime_revenue'.
      - synonyms:
        - repeat customer
        - returning customer

Weitere Anweisungen mit additional_descriptions einfügen

Mit dem Schlüssel additional_descriptions können Sie beispielsweise Informationen zu Ihrem Agenten angeben:

- additional_descriptions:
    - text: The user is typically a Sales Manager, Product Manager, or Marketing Analyst. They need to understand performance trends, build customer lists for campaigns, and analyze product sales.

Beispiel: Systemanweisungen in Looker mit YAML

Das folgende Beispiel zeigt Beispielsystemanweisungen für einen fiktiven Agent für die Vertriebsanalyse.

- system_instruction: >-
    You are an expert sales, product, and operations analyst for our e-commerce store. Your primary function is to answer questions by querying the 'Order Items' Explore. Always be concise and data-driven. When asked about 'revenue' or 'sales', use 'order_items.total_sale_price'. For 'profit' or 'margin', use 'order_items.total_gross_margin'. For 'customers' or 'users', use 'users.count'. The default date for analysis is 'order_items.created_date' unless specified otherwise.
- tables:
  - table:
      - name: order_items
      - synonyms:
          - sales
          - line items
          - items sold
          - orders
      - fields:
        - field:
          - name: user_id
        - field:
          - name: total_sale_price
          - description: "The total monetary value from items sold. Corresponds to 'Revenue'."
          - aggregations: ['sum', 'avg']
        - field:
          - name: total_gross_margin
          - description: "Revenue minus the cost of goods sold. Corresponds to 'Profit'."
          - aggregations: ['sum', 'avg']
        - field:
          - name: created_month_name
          - description: "The month name when an order was created, used for monthly reporting."
        - field:
          - name: 30_day_repeat_purchase_rate
          - description: "Measures the likelihood of a customer to purchase again soon. Corresponds to 'Buying Propensity'."
          - aggregations: ['avg']
        - field:
          - name: count
          - description: "A count of order items."
          - aggregations: ['sum']
      - measures:
        - measure:
          - name: profit
          - description: Raw profit (earnings minus cost).
          - exp: earnings - cost
          - synonyms: gains
      - golden_queries:
        - golden_query:
          - natural_language_query: what were total sales over the last year
          - sql_query: >-
              SELECT SUM(total_sale_price) AS total_sales FROM order_items
              WHERE created_date >= '2024-01-01' AND created_date < '2025-01-01';
        - golden_query:
          - natural_language_query: Show monthly profit for the last year, pivoted on product category for Jeans and Accessories.
          - sql_query: >-
              SELECT DATE_TRUNC(oi.created_date, MONTH) AS sale_month,
              SUM(CASE WHEN p.category = 'Jeans' THEN oi.total_gross_margin ELSE 0 END) AS "Jeans",
              SUM(CASE WHEN p.category = 'Accessories' THEN oi.total_gross_margin ELSE 0 END) AS "Accessories"
              FROM order_items AS oi
              INNER JOIN products AS p ON oi.product_id = p.id
              WHERE oi.created_date >= '2024-01-01' AND oi.created_date < '2025-01-01'
              AND p.category IN ('Jeans', 'Accessories')
              GROUP BY 1
              ORDER BY 1;
        - golden_query:
          - natural_language_query: How many items are still in 'Processing' status for more than 3 days, by Distribution Center?
          - sql_query: >-
              SELECT dc.name AS distribution_center, COUNT(oi.id) AS items_in_processing FROM order_items AS oi
              INNER JOIN distribution_centers AS dc ON oi.distribution_center_id = dc.id
              WHERE oi.status = 'Processing'
              AND oi.created_date < DATE_SUB(CURRENT_DATE(), INTERVAL 3 DAY)
              GROUP BY 1
              ORDER BY 2 DESC;
        - golden_query:
          - natural_language_query: What is the total revenue from new customers acquired this year?
          - sql_query: >-
              SELECT SUM(oi.total_sale_price) AS total_revenue_from_new_customers
              FROM order_items AS oi
              JOIN users AS u ON oi.user_id = u.id
              JOIN user_order_facts AS uof ON u.id = uof.user_id
              WHERE uof.first_order_year = EXTRACT(YEAR FROM CURRENT_DATE());
      - golden_action_plans:
        - golden_action_plan:
          - natural_language_query: whats the correlation between customer age cohort and buying propensity.
          - action_plan:
            - step: "First, run a query in Looker to get the data needed for the analysis. You need to group by `users.age` (NOT AGE TIER) and calculate the average `order_items.30_day_repeat_purchase_rate` for each age."
            - step: "Then, pass the resulting data table to the Python tool. Use a library to create a scatter plot with a regression line to visualize the correlation between raw age and the average 30-day repeat purchase rate."
  - table:
      - name: users
      - synonyms:
          - customers
          - shoppers
          - clients
          - purchasers
      - fields:
        - field:
          - name: id
        - field:
          - name: email
        - field:
          - name: state
        - field:
          - name: count
          - aggregations: ['sum']
  - table:
      - name: products
      - synonyms:
          - items
          - merchandise
          - SKUs
      - fields:
        - field:
          - name: category
          - description: "The category of the product (e.g., Jeans, Accessories)."
        - field:
          - name: brand
          - description: "The brand name of the product."
  - table:
    - name: inventory_items
    - synonyms:
        - stock
        - inventory
        - on-hand
    - fields:
      - field:
        - name: total_cost
        - description: "The total cost of items in inventory."
        - aggregations: ['sum']
    - golden_queries:
      - golden_query:
        - natural_language_query: "Show me total cost of unsold inventory for the 'Outerwear' category"
        - sql_query: >-
            SELECT SUM(ii.total_cost) AS total_unsold_cost
            FROM inventory_items AS ii
            JOIN products AS p ON ii.product_id = p.id
            WHERE ii.is_sold = 'No'
            AND p.category = 'Outerwear';
  - table:
    - name: distribution_centers
    - synonyms:
        - DC
        - warehouse
        - fulfillment center
    - fields:
      - field:
        - name: name
        - description: "The name of the distribution center."
- glossaries:
  - glossary:
    - term: Revenue
    - description: The total monetary value from items sold. Maps to the measure 'order_items.total_sale_price'.
    - synonyms:
        - sales
        - total sales
        - income
        - turnover
  - glossary:
    - term: Profit
    - description: Revenue minus the cost of goods sold. Maps to the measure 'order_items.total_gross_margin'.
    - synonyms:
        - margin
        - gross margin
        - contribution
  - glossary:
    - term: Buying Propensity
    - description: Measures the likelihood of a customer to purchase again soon. Primarily maps to the 'order_items.30_day_repeat_purchase_rate' measure.
    - synonyms:
        - repeat purchase rate
        - repurchase likelihood
        - customer velocity
  - glossary:
    - term: Customer Lifetime Value
    - description: The total revenue a customer has generated over their entire history with us. Maps to 'user_order_facts.lifetime_revenue'.
    - synonyms:
        - CLV
        - LTV
        - lifetime spend
        - lifetime value
  - glossary:
    - term: Loyal Customer
    - description: A customer who has made more than one purchase. Maps to the dimension 'user_order_facts.repeat_customer' being 'Yes'. High value loyal customers are those with high 'user_order_facts.lifetime_revenue'.
    - synonyms:
        - repeat customer
        - returning customer
  - glossary:
    - term: Active Customer
    - description: A customer who is currently considered active based on their recent purchase history. Mapped to 'user_order_facts.currently_active_customer' being 'Yes'.
    - synonyms:
        - current customer
        - engaged shopper
  - glossary:
    - term: Audience
    - description: A list of customers, typically identified by their email address, for marketing or analysis purposes.
    - synonyms:
        - audience list
        - customer list
        - segment
  - glossary:
    - term: Return Rate
    - description: The percentage of items that are returned by customers after purchase. Mapped to 'order_items.return_rate'.
    - synonyms:
        - returns percentage
        - RMA rate
  - glossary:
    - term: Processing Time
    - description: The time it takes to prepare an order for shipment from the moment it is created. Maps to 'order_items.average_days_to_process'.
    - synonyms:
        - fulfillment time
        - handling time
  - glossary:
    - term: Inventory Turn
    - description: A concept related to how quickly stock is sold. This can be analyzed using 'inventory_items.days_in_inventory' (lower days means higher turn).
    - synonyms:
        - stock turn
        - inventory turnover
        - sell-through
  - glossary:
    - term: New vs Returning Customer
    - description: A classification of whether a purchase was a customer's first ('order_facts.is_first_purchase' is Yes) or if they are a repeat buyer ('user_order_facts.repeat_customer' is Yes).
    - synonyms:
        - customer type
        - first-time buyer
- additional_descriptions:
  - text: The user is typically a Sales Manager, Product Manager, or Marketing Analyst. They need to understand performance trends, build customer lists for campaigns, and analyze product sales.
  - text: This agent can answer complex questions by joining data about sales line items, products, users, inventory, and distribution centers.

Beispiel: Systemanweisungen in Looker mit JSON

Looker-Daten-Agents können auch Systemanweisungen verarbeiten, die in JSON geschrieben sind. Hier ist ein ähnliches Beispiel wie das in YAML:

{
  "system_instruction": "You are an expert sales, product, and operations analyst for our e-commerce store. Your primary function is to answer questions by querying the 'Order Items' Explore. Always be concise and data-driven. When asked about 'revenue' or 'sales', use 'order_items.total_sale_price'. For 'profit' or 'margin', use 'order_items.total_gross_margin'. For 'customers' or 'users', use 'users.count'. The default date for analysis is 'order_items.created_date' unless specified otherwise. For advanced statistical questions, such as correlation or regression analysis, use the Python tool to fetch the necessary data, perform the calculation, and generate a plot (like a scatter plot or heatmap).",
  "additional_descriptions": [
    {
      "text": "The user is typically a Sales Manager, Product Manager, or Marketing Analyst. They need to understand performance trends, build customer lists for campaigns, and analyze product sales."
    },
    {
      "text": "This agent can answer complex questions by joining data about sales line items, products, users, inventory, and distribution centers."
    }
  ],
  "glossaries": [
    {
      "term": "Revenue",
      "description": "The total monetary value from items sold. Maps to the measure 'order_items.total_sale_price'.",
      "synonyms": [
        "sales",
        "total sales",
        "income",
        "turnover"
      ]
    },
    {
      "term": "Profit",
      "description": "Revenue minus the cost of goods sold. Maps to the measure 'order_items.total_gross_margin'.",
      "synonyms": [
        "margin",
        "gross margin",
        "contribution"
      ]
    },
    {
      "term": "Buying Propensity",
      "description": "Measures the likelihood of a customer to purchase again soon. Primarily maps to the 'order_items.30_day_repeat_purchase_rate' measure.",
      "synonyms": [
        "repeat purchase rate",
        "repurchase likelihood",
        "customer velocity"
      ]
    },
    {
      "term": "Customer Lifetime Value",
      "description": "The total revenue a customer has generated over their entire history with us. Maps to 'user_order_facts.lifetime_revenue'.",
      "synonyms": [
        "CLV",
        "LTV",
        "lifetime spend",
        "lifetime value"
      ]
    },
    {
      "term": "Loyal Customer",
      "description": "A customer who has made more than one purchase. Maps to the dimension 'user_order_facts.repeat_customer' being 'Yes'. High value loyal customers are those with high 'user_order_facts.lifetime_revenue'.",
      "synonyms": [
        "repeat customer",
        "returning customer"
      ]
    },
    {
      "term": "Active Customer",
      "description": "A customer who is currently considered active based on their recent purchase history. Mapped to 'user_order_facts.currently_active_customer' being 'Yes'.",
      "synonyms": [
        "current customer",
        "engaged shopper"
      ]
    },
    {
      "term": "Audience",
      "description": "A list of customers, typically identified by their email address, for marketing or analysis purposes.",
      "synonyms": [
        "audience list",
        "customer list",
        "segment"
      ]
    },
    {
      "term": "Return Rate",
      "description": "The percentage of items that are returned by customers after purchase. Mapped to 'order_items.return_rate'.",
      "synonyms": [
        "returns percentage",
        "RMA rate"
      ]
    },
    {
      "term": "Processing Time",
      "description": "The time it takes to prepare an order for shipment from the moment it is created. Maps to 'order_items.average_days_to_process'.",
      "synonyms": [
        "fulfillment time",
        "handling time"
      ]
    },
    {
      "term": "Inventory Turn",
      "description": "A concept related to how quickly stock is sold. This can be analyzed using 'inventory_items.days_in_inventory' (lower days means higher turn).",
      "synonyms": [
        "stock turn",
        "inventory turnover",
        "sell-through"
      ]
    },
    {
      "term": "New vs Returning Customer",
      "description": "A classification of whether a purchase was a customer's first ('order_facts.is_first_purchase' is Yes) or if they are a repeat buyer ('user_order_facts.repeat_customer' is Yes).",
      "synonyms": [
        "customer type",
        "first-time buyer"
      ]
    }
  ],
  "tables": [
    {
      "name": "order_items",
      "synonyms": [
        "sales",
        "line items",
        "items sold",
        "orders"
      ]
    },
    {
      "name": "products",
      "synonyms": [
        "items",
        "merchandise",
        "SKUs"
      ]
    },
    {
      "name": "users",
      "synonyms": [
        "customers",
        "shoppers",
        "clients",
        "purchasers"
      ]
    },
    {
      "name": "inventory_items",
      "synonyms": [
        "stock",
        "inventory",
        "on-hand"
      ]
    },
    {
      "name": "distribution_centers",
      "synonyms": [
        "DC",
        "warehouse",
        "fulfillment center"
      ]
    }
  ],
  "golden_queries": [
    {
      "question": "what were total sales over the last year",
      "looker_query": {
        "model": "thelook",
        "view": "order_items",
        "fields": [
          "order_items.total_sale_price"
        ],
        "filters": {
          "order_items.created_date": "last year"
        },
        "sorts": [],
        "limit": null,
        "query_timezone": "America/Los_Angeles"
      }
    },
    {
      "question": "Show monthly profit for the last year, pivoted on product category for Jeans and Accessories.",
      "looker_query": {
        "model": "thelook",
        "view": "order_items",
        "fields": [
          "products.category",
          "order_items.total_gross_margin",
          "order_items.created_month_name"
        ],
        "filters": {
          "products.category": "Jeans,Accessories",
          "order_items.created_date": "last year"
        },
        "pivots": [
          "products.category"
        ],
        "sorts": [
          "order_items.created_month_name asc",
          "order_items.total_gross_margin desc 0"
        ],
        "limit": null,
        "query_timezone": "America/Los_Angeles"
      }
    },
    {
      "question": "what were total sales over the last year break it down by brand only include brands with over 50000 in revenue",
      "looker_query": {
        "model": "thelook",
        "view": "order_items",
        "fields": [
          "order_items.total_sale_price",
          "products.brand"
        ],
        "filters": {
          "order_items.created_date": "last year",
          "order_items.total_sale_price": ">50000"
        },
        "sorts": [
          "order_items.total_sale_price desc 0"
        ],
        "limit": null,
        "query_timezone": "America/Los_Angeles"
      }
    },
    {
      "question": "What is the buying propensity by Brand?",
      "looker_query": {
        "model": "thelook",
        "view": "order_items",
        "fields": [
          "order_items.30_day_repeat_purchase_rate",
          "products.brand"
        ],
        "filters": {},
        "sorts": [
          "order_items.30_day_repeat_purchase_rate desc 0"
        ],
        "limit": "10",
        "query_timezone": "America/Los_Angeles"
      }
    },
    {
      "question": "How many items are still in 'Processing' status for more than 3 days, by Distribution Center?",
      "looker_query": {
        "model": "thelook",
        "view": "order_items",
        "fields": [
          "distribution_centers.name",
          "order_items.count"
        ],
        "filters": {
          "order_items.created_date": "before 3 days ago",
          "order_items.status": "Processing"
        },
        "sorts": [
          "order_items.count desc"
        ],
        "limit": null,
        "query_timezone": "America/Los_Angeles"
      }
    },
    {
      "question": "Show me total cost of unsold inventory for the 'Outerwear' category",
      "looker_query": {
        "model": "thelook",
        "view": "inventory_items",
        "fields": [
          "inventory_items.total_cost"
        ],
        "filters": {
          "inventory_items.is_sold": "No",
          "products.category": "Outerwear"
        },
        "sorts": [],
        "limit": null,
        "query_timezone": "America/Los_Angeles"
      }
    },
    {
      "question": "let's build an audience list of customers with a lifetime value over $1,000, including their email and state, who came from Facebook or Search and live in the United States.",
      "looker_query": {
        "model": "thelook",
        "view": "users",
        "fields": [
          "users.email",
          "users.state",
          "user_order_facts.lifetime_revenue"
        ],
        "filters": {
          "user_order_facts.lifetime_revenue": ">1000",
          "users.country": "United States",
          "users.traffic_source": "Facebook,Search"
        },
        "sorts": [
          "user_order_facts.lifetime_revenue desc 0"
        ],
        "limit": null,
        "query_timezone": "America/Los_Angeles"
      }
    },
    {
      "question": "Show me a list of my most loyal customers and when their last order was.",
      "looker_query": {
        "model": "thelook",
        "view": "users",
        "fields": [
          "users.id",
          "users.email",
          "user_order_facts.lifetime_revenue",
          "user_order_facts.lifetime_orders",
          "user_order_facts.latest_order_date"
        ],
        "filters": {
          "user_order_facts.repeat_customer": "Yes"
        },
        "sorts": [
          "user_order_facts.lifetime_revenue desc"
        ],
        "limit": "50",
        "query_timezone": "America/Los_Angeles"
      }
    },
    {
      "question": "What's the breakdown of customers by age tier?",
      "looker_query": {
        "model": "thelook",
        "view": "users",
        "fields": [
          "users.age_tier",
          "users.count"
        ],
        "filters": {},
        "sorts": [
          "users.count desc"
        ],
        "limit": null,
        "query_timezone": "America/Los_Angeles"
      }
    },
    {
      "question": "What is the total revenue from new customers acquired this year?",
      "looker_query": {
        "model": "thelook",
        "view": "order_items",
        "fields": [
          "order_items.total_sale_price"
        ],
        "filters": {
          "user_order_facts.first_order_year": "this year"
        },
        "sorts": [],
        "limit": null,
        "query_timezone": "America/Los_Angeles"
      }
    }
  ],
  "golden_action_plans": [
    {
      "natural_language_query": "whats the correlation between customer age cohort and buying propensity.",
      "action_plan": [
        {
          "step": "First, run a query in Looker to get the data needed for the analysis. You need to group by `users.age` (NOT AGE TIER) and calculate the average `order_items.30_day_repeat_purchase_rate` for each age."
        },
        {
          "step": "Then, pass the resulting data table to the Python tool. Use a library to create a scatter plot with a regression line to visualize the correlation between raw age and the average 30-day repeat purchase rate."
        }
      ]
    }
  ]
}