AI_AGG

AI_AGG(
  text_column,
  instruction
)

SELECT 
    product_id,
    AI_AGG(
        review_text,
        'Summarize the key themes in customer reviews'
    ) AS review_summary
FROM customer_reviews
GROUP BY

SELECT 
    department,
    AI_AGG(
        ticket_description,
        'List the top 3 most common issues mentioned'
    ) AS common_issues
FROM support_tickets
WHERE created_date >= CURRENT_DATE - 30
GROUP BY

SELECT 
    region,
    AI_AGG(
        sales_notes,
        'Identify potential upsell opportunities mentioned by sales team'
    ) AS opportunities
FROM sales_calls
WHERE call_date >= CURRENT_DATE - 90
GROUP BY

SELECT 
    AI_AGG(
        meeting_notes,
        'Create an executive summary highlighting decisions made and action items'
    ) AS executive_summary
FROM weekly_meetings
WHERE meeting_date >= DATE_TRUNC('month', CURRENT_DATE)

SELECT 
    product_category,
    AI_AGG(
        customer_feedback,
        'Summarize overall customer sentiment and provide specific examples of positive and negative feedback'
    ) AS sentiment_analysis
FROM feedback
GROUP BY

-- Good: Specific task description
AI_AGG(
    review_text,
    'Summarize customer feedback for an investor report, highlighting product strengths, weaknesses, and recommended improvements'
)

-- Less effective: Vague instruction
AI_AGG(review_text, 'Summarize')

-- Filter before aggregating for better relevance
SELECT 
    product_id,
    AI_AGG(
        review_text,
        'What are customers saying about product quality?'
    ) AS quality_feedback
FROM customer_reviews
WHERE rating <= 3  -- Focus on critical reviews
  AND created_date >= CURRENT_DATE - 90
GROUP BY

-- Group by meaningful dimensions
SELECT 
    product_category,
    price_range,
    AI_AGG(
        review_text,
        'Summarize value-for-money sentiment'
    ) AS value_perception
FROM reviews
GROUP BY product_category,

SELECT 
    product_id,
    COUNT(*) AS review_count,
    AVG(rating) AS avg_rating,
    AI_AGG(
        review_text,
        'Extract the most frequently mentioned feature (positive or negative)'
    ) AS key_feature
FROM reviews
GROUP BY product_id
HAVING COUNT(*) >= 10;  -- Ensure sufficient data

SELECT 
    competitor,
    AI_AGG(
        customer_comment,
        'What competitive advantages or disadvantages are mentioned?'
    ) AS competitive_analysis
FROM competitor_mentions
GROUP BY

SELECT 
    feature_request_category,
    AI_AGG(
        user_request,
        'Prioritize feature requests by frequency and impact'
    ) AS prioritized_requests
FROM feature_requests
GROUP BY