Snowflake Intelligence Guide: Setup, Optimization & Real SQL Examples

I’ve spent the last few days working with Snowflake Intelligence, and I want to share what actually works—not just the marketing pitch. If you’re ti

I’ve spent the last few days working with Snowflake Intelligence, and I want to share what actually works—not just the marketing pitch. If you’re tired of being the bottleneck for every data request in your organization, this might be exactly what you need.

Why This Actually Matters

Here’s the thing: most companies still treat data like it’s 2010. Your sales team wants to know last quarter’s performance by region? They file a ticket. Marketing needs customer segmentation data? Another ticket. By the time your data team gets through the backlog, the insights are already stale.

Snowflake Intelligence changes this dynamic. Instead of writing SQL, users ask questions in plain English. “Show me our top 10 customers by revenue this quarter” becomes a conversation, not a development task.

I was skeptical at first. Natural language querying isn’t new—we’ve all seen chatbots that completely miss the point. But the difference here is the architecture. The system uses AI agents that understand your specific business context, not generic SQL generation.

The Three Building Blocks

Understanding how this works helps you use it better. There are three key pieces:

Natural Language Processing (NLP) translates what you’re asking into something the system can work with. It’s not just keyword matching—it understands context. When someone asks about “Q4 performance,” it knows whether they mean fiscal or calendar year based on your company’s setup.

AI Agents are where the magic happens. Think of them as specialized assistants. Your finance agent knows the difference between GAAP revenue and recognized revenue. Your supply chain agent understands lead times and reorder points. You configure these agents to match how your business actually works.

Semantic Views sit between the agents and your raw data. They’re essentially curated views of your data that make sense to humans and AI alike. Instead of exposing 47 columns from your sales table, you create a semantic view with the 12 that actually matter for reporting.

Setting This Up (The Real Way)

Let me walk you through a realistic implementation. I’m using Snowflake’s sample data so you can follow along.

Step 1: Create Your Semantic View

Start simple. Here’s a semantic view built on Snowflake’s TPCH sample dataset:

-- First, get access to the sample data
USE DATABASE SNOWFLAKE_SAMPLE_DATA;
USE SCHEMA TPCH_SF1;

-- Create your own database for semantic views
CREATE DATABASE IF NOT EXISTS MY_INTELLIGENCE_DB;
CREATE SCHEMA IF NOT EXISTS MY_INTELLIGENCE_DB.SEMANTIC_LAYER;

-- Build a semantic view for customer orders
CREATE OR REPLACE VIEW MY_INTELLIGENCE_DB.SEMANTIC_LAYER.CUSTOMER_ORDERS AS
SELECT 
    c.C_CUSTKEY as customer_id,
    c.C_NAME as customer_name,
    c.C_MKTSEGMENT as market_segment,
    n.N_NAME as country,
    o.O_ORDERKEY as order_id,
    o.O_ORDERDATE as order_date,
    o.O_TOTALPRICE as order_total,
    o.O_ORDERSTATUS as order_status
FROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.CUSTOMER c
JOIN SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.ORDERS o 
    ON c.C_CUSTKEY = o.O_CUSTKEY
JOIN SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.NATION n 
    ON c.C_NATIONKEY = n.N_NATIONKEY
WHERE o.O_ORDERDATE >= '1995-01-01';

A database workspace shows SQL code creating a CUSTOMER_ORDERS view, with the database explorer on the left and query results at the bottom. A red arrow points to the SH schema in the explorer panel.

This view hides the complexity of joins and uses clear, business-friendly column names. Your AI agent will query this, not the raw tables.

Step 2: Add Performance Optimization

For views that get hit frequently,regular view makes a huge difference:

CREATE OR REPLACE VIEW MY_INTELLIGENCE_DB.SEMANTIC_LAYER.DAILY_SALES_SUMMARY AS
SELECT 
    DATE_TRUNC('day', order_date) AS sale_date,
    market_segment,
    country,
    COUNT(DISTINCT order_id) AS order_count,
    SUM(order_total) AS total_revenue,
    AVG(order_total) AS avg_order_value
FROM MY_INTELLIGENCE_DB.SEMANTIC_LAYER.CUSTOMER_ORDERS
GROUP BY 1, 2, 3;

A screenshot of a database workspace shows SQL code creating a view named DAILY_SALES_SUMMARY. An arrow points to DAILY_SALES_SUMMARY in the list of views. Query results and object explorer are visible.

Run this and check the results:

SELECT * 
FROM MY_INTELLIGENCE_DB.SEMANTIC_LAYER.DAILY_SALES_SUMMARY 
WHERE sale_date >= '1998-01-01'
ORDER BY total_revenue DESC
LIMIT 20;

A database query interface displays a SQL query and results table showing sales data by country and segment, with columns for sales name, market segment, country, order count, total revenue, and average revenue. Bar graphs visualize values.

Step 3: Configure Your AI Agent

When you set up an AI agent in Snowflake Intelligence, you give it specific instructions. Here’s what mine looks like for a sales agent:

Agent Name: Sales Analytics Agent

Instructions:

You have access to customer order data through the SEMANTIC_LAYER.CUSTOMER_ORDERS view.

When users ask about:
- "Revenue" or "sales" - use the order_total column
- "Customers" - always include customer_name and market_segment
- Time periods - default to the last 90 days unless specified
- "Top customers" - rank by total order_total, limit to 10 unless specified

Always format currency as USD with 2 decimal places.
If a query would scan more than 1 million rows, ask the user to narrow the date range.

What Actually Breaks (And How to Fix It)

I’ve seen these issues kill projects:

Vague Questions = Expensive Queries When someone asks “show me everything about customers,” the system might scan your entire data warehouse. Train your users to be specific: “Show me customers in the AUTOMOBILE segment who ordered more than $100k in 1998.”

Semantic Views That Drift Your source tables change. Columns get renamed. New status codes appear. Your semantic views break, and suddenly the AI returns garbage. Set up a weekly validation job:

-- Quick health check for your semantic views
SELECT 
    TABLE_SCHEMA,
    TABLE_NAME,
    LAST_ALTERED,
    ROW_COUNT
FROM MY_INTELLIGENCE_DB.INFORMATION_SCHEMA.TABLES
WHERE TABLE_SCHEMA = 'SEMANTIC_LAYER'
AND TABLE_TYPE = 'VIEW'
ORDER BY LAST_ALTERED DESC;

A database query interface powered by Snowflake Intelligence displays SQL code selecting table details from the SEMANTIC_LAYER schema. Below, results list two tables: DAILY_SALES_SUMMARY and CUSTOMER_ORDERS, with last altered dates and row counts.

Runaway Costs One enthusiastic user can rack up hundreds in compute charges with poorly scoped questions. Use resource monitors:

-- Create a resource monitor for your Intelligence workload
CREATE RESOURCE MONITOR INTELLIGENCE_BUDGET
WITH CREDIT_QUOTA = 100
FREQUENCY = MONTHLY
START_TIMESTAMP = IMMEDIATELY
TRIGGERS
    ON 75 PERCENT DO NOTIFY
    ON 100 PERCENT DO SUSPEND;

-- Assign it to your warehouse
ALTER WAREHOUSE INTELLIGENCE_WH SET RESOURCE_MONITOR = INTELLIGENCE_BUDGET;

Performance Tips That Actually Work

Clustering Keys If your semantic views filter by date constantly, cluster on that date:

-- Add clustering to improve query performance
ALTER TABLE MY_INTELLIGENCE_DB.SEMANTIC_LAYER.DAILY_SALES_SUMMARY
CLUSTER BY (sale_date);

Query Tagging for Cost Tracking Tag queries so you can see exactly what each agent costs:

-- At the start of an agent session
ALTER SESSION SET QUERY_TAG = 'sales_agent_q4_analysis';

-- Your queries here

-- View tagged query costs later
SELECT 
    QUERY_TAG,
    COUNT(*) as query_count,
    SUM(TOTAL_ELAPSED_TIME)/1000 as total_seconds,
    SUM(CREDITS_USED_CLOUD_SERVICES) as credits_used
FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY
WHERE QUERY_TAG IS NOT NULL
AND START_TIME >= DATEADD(day, -7, CURRENT_TIMESTAMP())
GROUP BY 1
ORDER BY credits_used DESC;

Test Queries to Validate Your Setup

Run these to make sure everything works:

-- Test 1: Basic aggregation
SELECT 
    market_segment,
    COUNT(DISTINCT customer_id) as customer_count,
    SUM(order_total) as total_revenue
FROM MY_INTELLIGENCE_DB.SEMANTIC_LAYER.CUSTOMER_ORDERS
WHERE order_date BETWEEN '1998-01-01' AND '1998-12-31'
GROUP BY 1
ORDER BY 2 DESC;

-- Test 2: Top customers
SELECT 
    customer_name,
    country,
    COUNT(order_id) as order_count,
    SUM(order_total) as lifetime_value
FROM MY_INTELLIGENCE_DB.SEMANTIC_LAYER.CUSTOMER_ORDERS
GROUP BY 1, 2
HAVING SUM(order_total) > 500000
ORDER BY 4 DESC
LIMIT 10;

-- Test 3: Time series
SELECT 
    DATE_TRUNC('month', order_date) as month,
    market_segment,
    SUM(order_total) as monthly_revenue
FROM MY_INTELLIGENCE_DB.SEMANTIC_LAYER.CUSTOMER_ORDERS
WHERE order_date >= '1997-01-01'
GROUP BY 1, 2
ORDER BY 1, 3 DESC;

The Bottom Line

Snowflake Intelligence isn’t magic, but it does work when you set it up right. Focus on clean semantic views, specific agent instructions, and cost controls from day one.

Start with one use case—maybe sales reporting or customer analytics. Get that working well before expanding. And involve your actual end users in testing. They’ll phrase questions in ways you never anticipated, and that feedback is gold.

The goal isn’t to eliminate your data team. It’s to free them from repetitive requests so they can focus on complex analysis and building better data products.

Further Reading: