genie-space-patterns

name	genie-space-patterns
description	Patterns for setting up Databricks Genie Spaces with comprehensive agent instructions, data assets, SQL expressions, and benchmark questions. Use when creating Genie Spaces, configuring agent behavior, selecting data assets, defining SQL expressions (measures, filters, dimensions), or validating benchmark questions. Includes mandatory 8-section deliverable structure, General Instructions (≤20 lines), data asset organization (Metric Views → TVFs → Tables), SQL expressions (sql_snippets) for structured KPI/filter/dimension definitions, benchmark questions with exact SQL, Serverless warehouse mandate, table/column comment requirements for Genie SQL quality, pre-creation table inspection, Conversation API programmatic validation, follow-up vs new conversation patterns, deployment checklists, post-deployment configuration audit for drift detection, cross-consumer design considerations (Genie + dashboards), and benchmark regression testing patterns.
metadata	{"author":"prashanth subrahmanyam","version":"2.6","domain":"semantic-layer","role":"worker","pipeline_stage":6,"pipeline_stage_name":"semantic-layer","called_by":["semantic-layer-setup"],"standalone":true,"last_verified":"2026-04-16","volatility":"medium","upstream_sources":[{"name":"ai-dev-kit","repo":"databricks-solutions/ai-dev-kit","paths":["databricks-skills/databricks-genie/SKILL.md"],"relationship":"extended","last_synced":"2026-02-19","sync_commit":"97a3637"}]}

Genie Space Patterns

Overview

This skill provides patterns for setting up production-ready Databricks Genie Spaces with natural language analytics capabilities. The quality of Genie responses directly correlates with the depth of business context provided in agent instructions.

Core Principle: Business context drives AI quality. Comprehensive agent instructions, properly selected data assets, and validated benchmark questions ensure reliable Genie performance.

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When to Use This Skill

Use this skill when:

• Creating new Genie Spaces for natural language analytics
• Configuring agent behavior and instructions
• Selecting and organizing data assets (Metric Views, TVFs, Tables)
• Writing benchmark questions for validation
• Troubleshooting Genie query routing issues
• Optimizing Genie Space performance

🔀 Hand Off to genie-space-export-import-api Skill When:

User Says / Task Involves	Load Instead
"deploy Genie Space via API"	genie-space-export-import-api
"export Genie Space", "download Genie Space config"	genie-space-export-import-api
"import Genie Space", "restore Genie Space"	genie-space-export-import-api
"CI/CD for Genie Spaces"	genie-space-export-import-api
"migrate Genie Space to another workspace"	genie-space-export-import-api
"back up Genie Space configuration"	genie-space-export-import-api
"programmatically create Genie Space from JSON"	genie-space-export-import-api
"serialized_space", "REST API", "/api/2.0/genie/spaces"	genie-space-export-import-api

This skill covers what goes into a Genie Space (instructions, assets, benchmarks). The export/import API skill covers how to deploy it programmatically.

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Upstream: Genie API Updates

The upstream databricks-genie skill provides these MCP tools:

Tool	Purpose
list_genie	List all Genie Spaces accessible to you
create_or_update_genie	Create or update a Genie Space
get_genie	Get Genie Space details
delete_genie	Delete a Genie Space
find_genie_by_name	Look up a Genie Space by name (when you don't have the space_id)
ask_genie	Ask a question to a Genie Space, get SQL + results
ask_genie_followup	Ask follow-up question in existing conversation

IMPORTANT: There is NO system table for Genie spaces (e.g., system.ai.genie_spaces does NOT exist). To find a Genie space by name, use the find_genie_by_name tool.

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Critical Rules

1. General Instructions Must Be ≤20 Lines

⚠️ CRITICAL: Genie processes General Instructions effectively only when ≤20 lines. Longer instructions get truncated or ignored.

✅ DO: Keep General Instructions concise and focused on essential routing rules.

❌ DON'T: Exceed 20 lines in General Instructions section.

2. Benchmark Questions Must Have Working SQL

Every benchmark question MUST include copy-paste-ready SQL that actually runs.

✅ DO: Include tested SQL with every benchmark question.

❌ DON'T: Provide questions without SQL or untested SQL.

⚠️ Temporal Expression Warning: Avoid CURRENT_DATE(), CURRENT_TIMESTAMP(), or DATE_TRUNC('month', CURRENT_DATE) in benchmark SQL. These produce different results each day, making automated regression testing unreliable.

❌ Fragile (non-deterministic):

WHERE transaction_date >= DATE_TRUNC('month', CURRENT_DATE)  -- Different results every day

✅ Stable for regression testing:

WHERE transaction_date BETWEEN DATE '2026-01-01' AND DATE '2026-03-31'  -- Pinned date range

Guidance: Use CURRENT_DATE - 30 for initial interactive testing, but pin to fixed date ranges (DATE '...') for CI/CD regression suites and benchmark validation scripts.

3. MEASURE() Uses Column Names, NOT Display Names

The MEASURE() function requires actual column name, NOT display_name.

❌ WRONG:

MEASURE(`Total Revenue`)  -- ❌ FAILS: "Total Revenue" is display_name

✅ CORRECT:

MEASURE(total_revenue)  -- ✅ Uses actual column name from YAML

⚠️ Dimension references in metric view queries must use bare names — NOT source/join table prefixes:

❌ WRONG:

SELECT dim_store.state_name, MEASURE(total_revenue)
FROM ${catalog}.${gold_schema}.revenue_metrics
WHERE dim_store.state_name = 'California'  -- ❌ UNRESOLVED_COLUMN
GROUP BY dim_store.state_name

✅ CORRECT:

SELECT state_name, MEASURE(total_revenue)
FROM ${catalog}.${gold_schema}.revenue_metrics
WHERE state_name = 'California'  -- ✅ Bare dimension name
GROUP BY state_name

Metric views flatten all dimensions into a single namespace. When writing benchmark SQL or General Instructions examples, always use bare dimension names without table prefixes.

4. Full UC 3-Part Namespace Required

All table and function references MUST use full Unity Catalog namespace.

❌ WRONG:

SELECT * FROM fact_sales;
SELECT * FROM get_revenue_by_period('2024-01-01', '2024-12-31', 'week');

✅ CORRECT:

SELECT * FROM ${catalog}.${gold_schema}.fact_sales;
SELECT * FROM ${catalog}.${gold_schema}.get_revenue_by_period('2024-01-01', '2024-12-31', 'week');

5. Data Asset Hierarchy: Metric Views → TVFs → Tables

Always add assets in this order:

1. Metric Views (Primary - use first)

   • Pre-aggregated, optimized, rich semantics
   • Best for broad analytical queries

2. TVFs (Secondary - use for specific patterns)

   • Parameterized queries, business logic
   • Date-bounded queries, top N rankings

3. Tables (Last resort - use sparingly)

   • Only when metric views/TVFs insufficient
   • Reference data, ad-hoc exploration

6. Avoid Contradictory Routing Rules

Issue: Contradictory rules cause Genie to randomly select wrong assets.

✅ DO: Group by question type, not asset

Revenue/booking questions:
  - By property → revenue_analytics_metrics
  - By host → get_host_performance TVF (not metric view!)

❌ DON'T: Create conflicting asset mappings

- host_analytics_metrics → for host data
- get_host_performance → for host data  # ❌ CONFLICT!

7. Define Ambiguous Terms Explicitly

Common ambiguous terms: "underperforming", "top performing", "valuable customers", "best hosts"

✅ DO: Add explicit definitions

## Term Definitions
"underperforming" = properties with revenue below median (use get_underperforming_properties TVF)
"top performing" = highest revenue unless "rated" specified

8. TVF Syntax Rules

Common errors to prevent:

❌ WRONG:

SELECT * FROM TABLE(get_customer_segments(...))  -- ❌ Don't wrap in TABLE()
SELECT * FROM get_customer_segments()            -- ❌ Missing parameters
SELECT * FROM get_customer_segments(...) GROUP BY segment  -- ❌ Unnecessary GROUP BY

✅ CORRECT:

SELECT * FROM get_customer_segments('2020-01-01', '2024-12-31')  -- ✅ Direct call with params

9. 🔴 MANDATORY: Serverless SQL Warehouse Only

ALWAYS assign a Serverless SQL Warehouse to Genie Spaces. NEVER use Classic or Pro warehouses.

Serverless provides auto-scaling, instant startup, and cost-efficient idle timedowns -- critical for interactive Genie sessions where users expect sub-10-second responses.

❌ WRONG: Classic SQL warehouse with manual cluster sizing.

✅ CORRECT: Serverless SQL warehouse (auto-detected or explicitly set).

10. Table/Column COMMENTs Are Genie Fuel

Genie uses Unity Catalog TABLE and COLUMN comments to understand data. Missing comments = degraded SQL generation quality.

🔴 MANDATORY: Before adding ANY table as a trusted asset, verify it has:

• COMMENT ON TABLE with a business-friendly description
• COMMENT ON COLUMN for every column, including dimension values and business context

See Table Documentation Skill for comment standards.

❌ WRONG:

CREATE TABLE fact_sales (sale_id BIGINT, amt DECIMAL(18,2));  -- No comments, cryptic names

✅ CORRECT:

CREATE TABLE fact_sales (
  sale_id BIGINT COMMENT 'Unique sale identifier from POS system',
  total_amount DECIMAL(18,2) COMMENT 'Net sale amount in USD after discounts'
) COMMENT 'Daily retail sales transactions at store-SKU grain';

11. Pre-Creation Table Inspection Is Mandatory

Before creating a Genie Space, ALWAYS inspect target table schemas. Do not rely on assumed schemas.

1. Run DESCRIBE TABLE EXTENDED or use get_table_details for each trusted asset
2. Verify all tables have TABLE and COLUMN comments
3. Verify descriptive column names (use customer_lifetime_value NOT clv)
4. Verify proper data types (DATE columns for time-based queries)

See Configuration Guide for the full inspection checklist.

12. Prompt User for Benchmark Questions Before Generating

Always ask the user for benchmark questions before generating synthetic ones. User-provided questions reflect real business needs and catch domain-specific edge cases that synthetic generation misses.

Three outcomes:

• User provides 10+: Validate each one. Report any that can't be answered (missing table, ambiguous terms). Proceed with valid set.
• User provides 1-9: Validate provided, report issues, augment with synthetic to reach 10-15 total. Show augmentation to user.
• User provides none: Generate 10-15 synthetic benchmarks from asset metadata. Show to user for review.

If a user question can't be answered, do NOT silently drop it. Inform the user with the specific reason:

• "Table X is not a trusted asset in this space"
• "No data available for churn analysis — available domains are: revenue, bookings, property performance"
• "Term 'underperforming' is ambiguous — how should it be defined?"

See Benchmark Intake Workflow for the full validation and generation pipeline.

13. Validate Programmatically via Conversation API

After deployment, test benchmark questions programmatically using the Conversation API -- not just the UI.

# ✅ Programmatic validation (reproducible, automated)
result = ask_genie(space_id="your_space_id", question="What were total sales last month?")
assert result["status"] == "COMPLETED"
assert result["row_count"] > 0

Key rules:

• Start a NEW conversation for each unrelated benchmark question
• Use ask_genie_followup ONLY for related follow-up questions within the same topic
• Set timeouts: simple queries (30s), complex joins (60-120s), large scans (120s+)

See Configuration Guide for full testing patterns.

14. Extended Instructions Must Follow the 13-Section Structure

⚠️ CRITICAL: For domains beyond simple use cases, structure Extended Instructions into these 13 mandatory sections:

PURPOSE → ASSET ROUTING → BUSINESS DEFINITIONS → DISAMBIGUATION → AGGREGATION RULES → FUNCTION ROUTING → JOIN GUIDANCE → QUERY RULES → QUERY PATTERNS → TEMPORAL FILTERS → DATA QUALITY NOTES → CONSTRAINTS → SQL EXPRESSIONS

Each section serves a distinct purpose for Genie's SQL generation. Missing sections (especially ASSET ROUTING, DISAMBIGUATION, and BUSINESS DEFINITIONS) are the top causes of misrouted queries. Section 13 (SQL EXPRESSIONS) documents which concepts to promote into structured sql_snippets.

✅ DO: Follow the 13-section template in Agent Instructions Guide.

❌ DON'T: Write instructions as an unstructured wall of text or a flat numbered list without section headers.

15. Column Configs Require Genie-Specific Flags

Every column_config entry should include enable_format_assistance and/or enable_entity_matching flags based on column type.

Column Type	enable_format_assistance	enable_entity_matching
Dimension text/name	✅	✅
Categorical flag (Y/N)	✅	✅
Date/timestamp	✅	❌
Numeric ID	✅	❌
Numeric measure	❌	❌

See Agent Instructions Guide for the full pattern.

16. Synonyms Go in Column Configs, Not Table COMMENTs

Synonyms belong in Genie Space column_configs[].synonyms or metric view YAML synonyms fields. Never embed synonyms in Unity Catalog TABLE or COLUMN COMMENT strings.

• UC COMMENTs → business definitions, grain, valid values
• column_configs synonyms → user-friendly alternative names for NLQ matching

17. Append Instructions When Optimizing — Never Replace

When optimizing an existing Genie Space, APPEND new rules to the existing instruction block. NEVER replace the entire block.

Existing rules were validated against benchmarks. Replacing them risks regression on questions that currently work. See Agent Instructions Guide.

18. Validate Programmatically via Conversation API

After deployment, test benchmark questions programmatically using the Conversation API — not just the UI.

result = ask_genie(space_id="your_space_id", question="What were total sales last month?")
assert result["status"] == "COMPLETED"
assert result["row_count"] > 0

Key rules:

• Start a NEW conversation for each unrelated benchmark question
• Use ask_genie_followup ONLY for related follow-up questions within the same topic
• Set timeouts: simple queries (30s), complex joins (60-120s), large scans (120s+)

See Configuration Guide for full testing patterns.

19. SQL Expressions Provide Structured Business Concept Definitions

SQL Expressions (sql_snippets in the API) give Genie structured, parseable definitions of measures, filters, and dimensions. Unlike text instructions (free-form) and example SQL queries (full query templates), SQL expressions define individual reusable concepts that Genie can match directly to user questions.

Type	JSON Key	SQL Requirement	Example
Measure	measures	Aggregation function	SUM(table.total_sales_usd)
Filter	filters	Boolean condition	table.country_code = 'US'
Dimension	expressions	Column reference or derivation (no aggregation)	table.zone_combination

When to use SQL Expressions:

• KPIs that users ask about frequently (promote from BUSINESS DEFINITIONS)
• Common WHERE clauses that appear in 3+ benchmark queries (promote from AGGREGATION RULES)
• Grouping attributes with synonyms (promote from DISAMBIGUATION section)

Every SQL Expression MUST include:

• display_name: User-friendly name shown in Genie UI
• sql: Working SQL fragment referencing trusted asset table.column
• synonyms: 2-5 alternative terms users might use
• instruction: When and how Genie should apply this expression

SQL Expressions complement text instructions — they do NOT replace them. Text instructions provide routing logic and disambiguation context. SQL expressions provide structured, matchable KPI/filter/dimension definitions. Use both together.

See Configuration Guide for JSON schema, examples, and the "promote" pattern.

20. Validate Benchmark SQL Dialect (Databricks SQL Only)

LLMs trained on multi-dialect SQL corpora often generate Oracle/Postgres/T-SQL syntax in benchmark SQL. Scan ALL benchmark and sql_snippet SQL for these common traps:

Wrong (Oracle/Postgres/T-SQL)	Correct (Databricks SQL)
TRUNC(date, 'quarter')	DATE_TRUNC('quarter', date)
SYSDATE	CURRENT_DATE()
NVL(x, y)	COALESCE(x, y)
DATEADD(day, -30, date)	DATE_ADD(date, -30)
DATEDIFF(day, d1, d2)	DATEDIFF(d2, d1)
TO_DATE('...')	CAST('...' AS DATE)

See Troubleshooting Guide for the full dialect reference.

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Quick Reference

Mandatory 8-Section Structure

Every Genie Space setup MUST produce a document with ALL 8 sections:

Section	Requirement	Key Constraint
A. Space Name	{Project} {Domain} Analytics Space	Exact format
B. Space Description	2-3 sentences	Business context
C. Sample Questions	10-15 questions	Grouped by domain
D. Data Assets	All tables & metric views	Table format
E. General Instructions	≤20 LINES	CRITICAL LIMIT
F. TVFs	All functions with signatures	Detailed specs
G. Benchmark Questions	10-15 with EXACT SQL	Working SQL required
H. SQL Expressions	Measures, Filters, Dimensions	Structured sql_snippets

🔴 Missing any section = INCOMPLETE deliverable. NO EXCEPTIONS.

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Core Setup Pattern

Step 1: Create Space Document Structure

Follow the mandatory 8-section structure (A-H). Use the configuration template as a starting point.

Step 2: Write General Instructions (≤20 Lines)

Use the template from Agent Instructions Guide:

## General Instructions

You are an expert {domain} analyst. Follow these rules:

1. **Primary Data Source:** Always use Metric Views first
2. **Use TVFs:** For common queries, prefer Table-Valued Functions
3. **Date Defaults:** If no date specified, default to last 30 days
4. **Aggregations:** Use SUM for totals, AVG for averages
5. **Sorting:** Sort by primary metric DESC unless specified
6. **Limits:** Return top 10-20 rows for ranking queries
7. **Currency:** Format as USD with 2 decimal places
8. **Percentages:** Show as % with 1 decimal place
9. **Synonyms:** Handle common term equivalents
10. **Context:** Explain results in business terms
11. **Comparisons:** Show absolute values and % difference
12. **Time Periods:** Support today, yesterday, last week, month, quarter, YTD
13. **Null Handling:** Exclude nulls from calculations
14. **Performance:** Never scan raw Bronze/Silver tables
15. **Accuracy:** State assumptions when uncertain

Step 3: Document Data Assets

Add assets in order: Metric Views → TVFs → Tables. Document each with:

• Metric Views: Measures, dimensions, use cases
• TVFs: Signature, parameters, return schema, use cases
• Tables: Purpose, when to use

See Configuration Guide for detailed patterns.

Step 3b: Define SQL Expressions

After documenting data assets, identify key business concepts to register as structured SQL Expressions (sql_snippets). These give Genie parseable definitions it can match directly against user questions.

Promote from text instructions:

1. Top KPIs (from BUSINESS DEFINITIONS) → measures with aggregation SQL and synonyms
2. Common WHERE clauses (from AGGREGATION RULES) → filters with boolean SQL and synonyms
3. Key grouping attributes (from DISAMBIGUATION) → expressions (dimensions) with column SQL and synonyms

UI terminology → JSON key mapping:

Databricks UI	JSON key in sql_snippets
Measure	measures
Filter	filters
Dimension	expressions

See Configuration Guide for JSON schema, field requirements, and build script patterns.

Step 4: Write Benchmark Questions (Interactive Intake)

Benchmark questions validate that Genie routes correctly and generates accurate SQL. Always prompt the user for their questions first before generating synthetic ones.

Three-Path Workflow

User Provides	Action
10+ questions	Validate each against available assets. Report any that can't be answered (missing table, ambiguous terms, need more info). Proceed with valid set.
1-9 questions	Validate provided questions. Report issues. Augment with synthetic benchmarks to reach 10-15 total.
No questions	Generate 10-15 synthetic benchmarks from metric view measures, TVF signatures, and table schemas. Show to user for review.

Validation Rules for User-Submitted Questions

For each submitted question, verify:

Check	What to Verify	If Failed, Tell the User
Asset coverage	At least one MV/TVF/table can answer it	"No asset in this space can answer '{question}'. Available domains: {list}."
Table existence	SQL only references trusted assets	"SQL references {table} which isn't a trusted asset."
MEASURE() columns	Column names match actual MV columns	"MEASURE({col}) doesn't match any column. Available: {list}."
UC namespace	SQL uses ${catalog}.${gold_schema}.{object}	"SQL must use full 3-part UC namespace."
TVF parameters	All required parameters present	"TVF {name} requires: {params}. Missing: {missing}."
Ambiguous terms	Terms like "underperforming", "top" are defined	"What does '{term}' mean here? Revenue? Ratings? Bookings?"

If a question cannot be answered (missing table, no matching data), inform the user with the specific reason and suggest alternatives based on available assets.

If a question needs clarification (ambiguous terms, missing time range), ask the user to clarify before including it.

Synthetic Generation (When User Provides None or Few)

Generate benchmarks from available asset metadata:

1. From Metric Views — one aggregation question per measure, one grouped question per dimension
2. From TVFs — one question per function matching its use case (ranking, time-series, detail)
3. From Tables — list/detail questions for dimension tables (only if needed to fill gaps)
4. Category coverage — ensure at least 4 categories: aggregation, ranking, time-series, comparison, list

After generation, show the synthetic benchmarks to the user for review before proceeding.

Augmentation (When User Provides Partial Set)

When augmenting user-provided questions:

1. User questions always take priority — never replace them
2. Fill category gaps first — if user only provided aggregation questions, add ranking/time-series/list
3. Add synonym variations — test Genie handles "total spend" vs "how much spent" vs "total costs"
4. Add date variations — "this month" vs "last 30 days" vs "Q1 2026"
5. Cap at 15 total — user questions + synthetic augmentation

See Benchmark Intake Workflow for the full validation pipeline, generation patterns, and augmentation strategy.

Output Format for Each Benchmark

Every question must include:

• Natural language question
• Expected SQL (tested and working)
• Expected result description

⚠️ JSON field name: In the API-compatible JSON export, benchmark SQL goes in answer: [{format: "SQL", content: ["SELECT ..."]}] — NOT in a field called expected_sql. The term "Expected SQL" here refers to the content, not the JSON field name. See genie-space-export-import-api for the exact BenchmarkQuestion schema.

See Configuration Guide for format.

Step 5: Deploy and Test

Choose your deployment path:

Method	When to Use	Skill
UI	One-off setup, manual curation	This skill (continue below)
REST API / CI/CD	Automated deployment, cross-workspace migration, version control	Load genie-space-export-import-api

UI deployment steps:

1. Inspect all target table schemas (verify comments, column names, data types)
2. Create Genie Space in Databricks UI with Serverless SQL Warehouse
3. Add trusted assets in order (Metric Views → TVFs → Tables). Production: Gold layer only. Workshop deployments may reference Silver/Bronze directly — Genie will work, but accuracy is typically lower because raw layers usually lack curated COMMENTs and dimensional joins. Promote to Gold for production hardening.
4. Set General Instructions (copy exactly, verify ≤20 lines)
5. Test benchmark questions programmatically via Conversation API
6. Validate routing, response quality, and follow-up context

API deployment steps: Load the genie-space-export-import-api skill for:

• JSON schema structure (serialized_space format)
• Template variable substitution (${catalog}, ${gold_schema})
• Asset inventory-driven generation (prevents "table doesn't exist" errors)
• Export/import scripts (export_genie_space.py, import_genie_space.py)

See Configuration Guide for complete steps.

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Reference Files

Detailed guides are available in the references/ directory:

Configuration Guide

Complete guide for the mandatory 8-section structure:

• Section A-H detailed formats (including SQL Expressions)
• Extended space description patterns
• Data asset organization patterns
• Column config flags for Genie
• Testing and validation procedures
• Deployment checklist
• Success metrics

Agent Instructions Guide

Comprehensive patterns for writing effective instructions:

• Extended instructions template (13-section structure, optional)
• General Instructions consistency patterns
• Ambiguous term definitions
• Metric View vs TVF routing decision table
• TVF syntax guidance
• Professional language standards
• Column config flags for Genie
• Enrichment script pattern
• Instruction evolution (append-only updates)

Troubleshooting Guide

Common issues, debugging steps, and verification procedures:

• Common routing issues and solutions
• MEASURE() function errors
• UC namespace problems
• TVF syntax errors
• Debugging procedures
• Verification checklists

Benchmark Intake Workflow

Interactive workflow for accepting, validating, and augmenting benchmark questions:

• Three-path intake (full, partial, none)
• Per-question validation pipeline (asset coverage, table existence, MEASURE() columns)
• Synthetic generation from asset metadata
• Augmentation strategy with category coverage
• User feedback templates for issues and suggestions

Trusted Assets Guide

Complete guide for organizing and documenting data assets:

• Metric View documentation patterns
• TVF documentation patterns
• Asset selection best practices
• Performance considerations
• Asset organization checklist

Genie Space Export - Formatted

Production-grade Genie Space export JSON (pretty-printed) showing the complete GenieSpaceExport schema:

• Full serialized_space structure with parsed objects (not nested string)
• All sections: config.sample_questions, data_sources (tables, metric_views), instructions (text, sql_functions, join_specs, sql_snippets), benchmarks
• Column configs with get_example_values and build_value_dictionary patterns
• Join specs with relationship type annotations (--rt=FROM_RELATIONSHIP_TYPE_MANY_TO_ONE--)
• SQL Expressions (sql_snippets) with sample measures, filters, and dimensions including synonyms and instructions
• Benchmark questions with complete SQL answers using MEASURE() syntax
• Use as the primary reference when creating new Genie Spaces programmatically
• Uses ${catalog}, ${gold_schema}, ${warehouse_id}, ${space_id}, ${project_name} template variables

Genie Space Export - Compact

Same Genie Space export in original API format (compact, serialized_space as escaped string):

• Exact format returned by GET /api/2.0/genie/spaces/{space_id}
• serialized_space field is a JSON string (not parsed object)
• Use as the reference for understanding raw API response format
• Useful for import/export scripts and CI/CD pipelines

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Assets

Scripts

Audit Genie Space

Automated drift detection script comparing live Genie Space config against version-controlled JSON:

• Exports live config via Genie REST API
• Diffs trusted assets, instructions, SQL expressions, benchmarks, and warehouse assignment
• Generates markdown audit report with severity levels
• --fail-on-drift flag for CI/CD gate integration

Templates

Genie Space Config Template

Starter YAML template for structuring Genie Space setup documents:

• All 8 sections with placeholders (A through H, including SQL Expressions)
• Deployment configuration
• Testing and training checklists

Benchmark Regression Test Template

Benchmark regression testing template using the Conversation API:

• Runs benchmark questions against a deployed Genie Space
• Compares returned SQL structure (fragments, table references) against expectations
• Validates row counts within expected ranges
• Generates pass/fail report by category
• --fail-threshold flag for CI/CD pass rate gates
• Configurable per-question timeout

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Validation Checklist

Before submitting ANY Genie Space document:

Section	Requirement	Complete?
A. Space Name	Exact name in format {Project} {Domain} Analytics Space	☐
B. Space Description	2-3 sentences describing purpose and users	☐
C. Sample Questions	10-15 questions grouped by domain	☐
D. Data Assets	ALL metric views, dimensions, facts in table format	☐
E. General Instructions	≤20 lines of LLM behavior rules	☐
F. TVFs	ALL functions with signatures and examples	☐
G. Benchmark Questions	10-15 questions with EXACT working SQL	☐
H. SQL Expressions	Measures, filters, and dimensions as structured sql_snippets	☐

Additional Quality Checks

• General Instructions are EXACTLY 20 lines or less (not 21+)
• Extended Instructions follow the 13-section structure (PURPOSE through SQL EXPRESSIONS)
• DISAMBIGUATION section explicitly resolves ambiguous columns (e.g., _combination vs _name)
• User was prompted for benchmark questions before synthetic generation
• User-submitted questions validated against available assets
• Invalid user questions reported back with specific reasons (not silently dropped)
• Benchmark suite has 10-15 questions with 4+ categories covered
• Every benchmark question has copy-paste-ready SQL
• SQL in benchmarks actually runs (tested)
• MEASURE() uses actual column names (not display_name with backticks)
• All tables/functions have full 3-part UC namespace
• Metric views documented with measures and dimensions
• TVFs documented with parameters, returns, and use cases
• Questions cover all major use cases (revenue, performance, trends)
• No contradictory routing rules in General Instructions
• Ambiguous terms explicitly defined
• Serverless SQL Warehouse assigned (NOT Classic or Pro)
• ALL trusted asset tables have TABLE and COLUMN comments
• Column names are descriptive (customer_lifetime_value NOT clv)
• Table schemas inspected before space creation (DESCRIBE TABLE EXTENDED)
• Benchmark questions validated programmatically via Conversation API
• Only Gold layer tables/views/functions used as trusted assets
• Column configs include enable_format_assistance / enable_entity_matching flags per column type
• Synonyms are in column_configs[].synonyms or metric view YAML — NOT in UC COMMENTs
• Instruction updates to existing spaces are appended, not replaced (unless full migration with regression testing)
• SQL Expressions (sql_snippets) reference only trusted asset tables
• Measures use aggregation functions (SUM, AVG, COUNT, etc.)
• Filters evaluate to boolean conditions
• Dimensions reference columns or derive per-row values (no aggregation)
• Each SQL Expression has display_name, instruction, and 2-5 synonyms
• Top KPIs from BUSINESS DEFINITIONS promoted to sql_snippets.measures

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Post-Deployment Configuration Audit

Genie Spaces drift in production — UI edits change instructions, assets are added/removed manually, benchmark questions get deleted. Use a version-controlled audit workflow to detect drift.

Audit Workflow

1. Export live config: Use the Genie API to export the current space as JSON (GET /api/2.0/genie/spaces/{space_id})
2. Diff against source: Compare the exported JSON to the version-controlled config in src/{project}_semantic/genie_configs/
3. Report drift: Flag any differences in trusted assets, instructions, sample questions, or warehouse assignment
4. Remediate: Either update the source JSON (if the UI changes were intentional) or re-deploy from source (if unintentional)

What to Audit

Check	How to Detect	Severity
Trusted assets changed	Diff data_sources array in exported JSON	🔴 High — affects query routing
General Instructions edited past 20 lines	Count lines in instructions.text	🔴 High — causes instruction truncation
Benchmark questions removed	Diff benchmarks array	🟡 Medium — reduces validation coverage
SQL Expressions modified	Diff instructions.sql_snippets	🔴 High — affects KPI/filter matching
Warehouse changed to non-Serverless	Check warehouse_id type	🔴 High — degrades interactive performance
Sample questions modified	Diff config.sample_questions	🟢 Low — cosmetic impact

Audit Cadence

Environment	Cadence	Trigger
Development	After each manual UI edit	Manual
Staging	Before promotion to production	CI/CD gate
Production	Weekly	Scheduled

Use scripts/audit_genie_space.py for automated detection. See Configuration Guide for detailed patterns.

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Cross-Consumer Design Considerations

Genie Spaces don't exist in isolation — the same Metric Views and TVFs often feed AI/BI dashboards, notebooks, and Power BI. Design assets with all consumers in mind.

Genie vs Dashboard Consumption Patterns

Consumer	How It Uses Metric Views	Design Implication
Genie / ad-hoc SQL	MEASURE(ratio_metric) — context-aware	Composed ratios work directly
Dashboard KPI counters	MEASURE(ratio_metric) — single value	Composed ratios work directly
Dashboard pivot tables	Raw dataset rows with widget-level aggregation	Requires additive components (numerator + denominator as separate measures)
Dashboard flat tables	Either pattern	No special handling needed

Key Design Rule

When a Metric View serves both Genie and dashboard pivot tables, always define both the composed ratio AND the additive components:

measures:
  # Additive components (consumed by dashboard pivots)
  - name: total_sales
    expr: SUM(source.net_sales_usd)
  - name: store_day_count
    expr: SUM(source.store_day_count)

  # Composed ratio (consumed by Genie, KPI widgets)
  - name: avg_sales_per_store_day
    expr: MEASURE(total_sales) / NULLIF(MEASURE(store_day_count), 0)

Asset Naming for Cross-Consumer Use

• Use consistent naming across Metric View YAML name fields and Genie Space data asset documentation
• Genie relies on synonyms for NLQ matching — keep these in column_configs or YAML, not in UC COMMENTs
• Dashboard pivot widgets use the column name directly — ensure they are human-readable (total_revenue, not rev_tot)

Geographic Dimensions for Downstream Dashboards

If Metric View dimensions will be used in Lakeview choropleth map widgets, use full geographic names (e.g., state_name: "California", not state_abbr: "CA"). Mapbox geographic roles require full text names. Apply a CASE expression in the upstream Gold MV or metric view if source data uses abbreviations.

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Common Mistakes to Avoid

Mistake	Why It Fails
General Instructions > 20 lines	Genie won't process effectively past 20 lines
Benchmark questions without SQL	Cannot validate Genie responses
Display names in MEASURE()	Requires actual column names from YAML
Partial UC namespaces	Always use full 3-part: ${catalog}.${schema}.{object}
Only tables as trusted assets	Start with Metric Views for better performance
Contradictory routing rules	Group by question type, not asset
Undefined ambiguous terms	Define "underperforming", "top performing" explicitly
Incorrect TVF syntax	No TABLE() wrapper, include all params, no extra GROUP BY
Classic/Pro SQL Warehouse	ALWAYS use Serverless (fast startup, auto-scaling)
Tables without comments	Genie uses UC metadata; missing = worse SQL
Cryptic column names (clv)	Use descriptive (customer_lifetime_value) for accuracy
Skipping table inspection	DESCRIBE TABLE EXTENDED before adding assets
UI-only testing	Validate programmatically via Conversation API
Reusing conversations across topics	New conversation per topic; ask_genie_followup only for related
Silver/Bronze as trusted assets in production Genie Spaces	Gold layer only for production — Silver/Bronze typically lack curated COMMENTs and dimensional joins required for high-quality NL accuracy. Workshop mode may deploy Genie Spaces directly on Silver/Bronze with a quality advisory; this is allowed for enablement and prototyping, but promote to Gold before going to production.
Unstructured instruction wall of text	Use the 13-section structure (PURPOSE through SQL EXPRESSIONS)
Missing DISAMBIGUATION section	Ambiguous columns (e.g., zone_name vs zone_combination) cause wrong GROUP BY
Replacing entire instruction block during optimization	Append new rules; replacement risks regression on validated benchmarks
Synonyms embedded in UC COMMENTs	Synonyms go in column_configs or metric view YAML — not in COMMENTs
Missing enable_format_assistance / enable_entity_matching	These flags significantly improve Genie's entity matching and formatting
KPI definitions only in text instructions	Also register as sql_snippets.measures for structured Genie matching
Filter SQL that isn't boolean	sql_snippets.filters must evaluate to boolean (e.g., table.col = 'value')
Dimension SQL with aggregation	sql_snippets.expressions must be per-row (column reference or CASE WHEN), never SUM/AVG
Missing synonyms on SQL Expressions	Synonyms are how Genie matches user terms to expressions; without them, matching fails
SQL Expressions referencing non-trusted tables	sql field must reference tables in data_sources.tables[].identifier

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References

Official Databricks Documentation

• Genie Overview
• Create a Genie Space
• Build a Knowledge Store (SQL Expressions)
• Add Trusted Assets
• Query with Genie
• Metric Views Documentation

Related Skills

• genie-space-export-import-api - Programmatic deployment, export/import, CI/CD, migration via REST API
• metric-views-patterns - Metric view YAML structure
• databricks-table-valued-functions - TVF patterns
• databricks-asset-bundles - Asset Bundle deployment

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Version History

• v2.6 (Apr 2026) — Production hardening from cross-skill comparison

  • Added MEASURE() bare dimension name guidance (Rule 3) — metric views flatten dimensions into a single namespace; table prefixes cause UNRESOLVED_COLUMN
  • Added benchmark SQL temporal expression warnings (Rule 2) — CURRENT_DATE() makes regression testing non-deterministic; pin date ranges for CI/CD
  • Added Post-Deployment Configuration Audit section — structured workflow to detect drift from manual UI edits (trusted assets, instructions, sql_snippets, benchmarks)
  • Added Cross-Consumer Design Considerations section — Genie vs dashboard pivot tables require additive components alongside composed ratios; geographic dimension naming for Lakeview choropleths
  • New script: scripts/audit_genie_space.py — automated drift detection comparing live API export against version-controlled JSON, with --fail-on-drift for CI/CD gates
  • New template: assets/templates/genie-space-regression-test.py — benchmark regression testing via Conversation API with SQL structure comparison, row count validation, and pass rate thresholds
  • Updated references/troubleshooting.md v1.2 — Added Issue 11 (Benchmark SQL Non-Determinism) and Issue 12 (Metric View Dimension Loss After Edits)
  • Updated references/configuration-guide.md — Added Post-Deployment Audit section with CI/CD integration patterns
  • Updated references/benchmark-intake-workflow.md — Added Regression Testing Pattern section with temporal expression policy and SQL structure comparison guidance
  • Key Learning: Production Genie Spaces drift through UI edits; version-controlled audit + regression testing catches drift before it impacts users

• v2.5 (Apr 2026) — SQL Expressions support

  • Added Rule 19: SQL Expressions provide structured business concept definitions
  • Expanded deliverable from 7-section to 8-section structure (added Section H: SQL Expressions)
  • Added Step 3b in Core Setup Pattern: Define SQL Expressions
  • Added sql_snippets to reference JSON export (genie_space_export_formatted.json)
  • Extended Instructions updated from 12-section to 13-section (added Section 13: SQL EXPRESSIONS)
  • Added cross-references in Sections 3 (BUSINESS DEFINITIONS) and 6 (FUNCTION ROUTING)
  • Added SQL Expressions for Assets section in Trusted Assets guide
  • Added Section H template in genie-space-config.yaml
  • Updated validation checklist with 7 new SQL Expression checks
  • Added 5 new Common Mistakes related to SQL Expressions
  • Added Knowledge Store docs link to References
  • Key Learning: SQL Expressions (sql_snippets) give Genie structured, matchable definitions that complement free-form text instructions; "promote" top KPIs from text → structured for better matching
  • API format: sql_snippets has three arrays: measures, filters, expressions (dimensions) — note the UI label "Dimension" maps to JSON key expressions

• v2.3 (Feb 2026) — JSON field name clarification for benchmark SQL (answer vs expected_sql)

• v2.2 (Feb 20, 2026) - Interactive benchmark question intake

  • Added Rule 12: Prompt user for benchmark questions before generating synthetic ones
  • Added three-path benchmark intake workflow (full, partial, none)
  • Added per-question validation pipeline (asset coverage, table existence, MEASURE() columns, ambiguity detection)
  • Added synthetic benchmark generation from asset metadata (MVs, TVFs, tables)
  • Added augmentation strategy for partial submissions with category coverage
  • Added user feedback templates for invalid/needs-info questions
  • New reference: benchmark-intake-workflow.md
  • Updated Step 4 with interactive intake flow
  • Updated validation checklist with benchmark intake checks
  • Renumbered Rule 12→13 (Conversation API validation)
  • Key Learning: User-provided questions catch domain-specific edge cases that synthetic generation misses; always ask first

• v2.1 (Feb 6, 2026) - Genie reference material integration

  • Added Rule 9: Serverless SQL Warehouse mandatory
  • Added Rule 10: Table/Column COMMENT requirements for Genie
  • Added Rule 11: Pre-creation table inspection mandatory
  • Added Rule 12: Programmatic validation via Conversation API
  • Added Gold-layer-only trusted assets mandate
  • Added descriptive column naming requirement
  • Added follow-up vs new conversation pattern
  • Added 8 new Common Mistakes to Avoid
  • Updated validation checklist with 6 new checks
  • Updated deployment steps with inspection and API testing
  • Key Learning: Genie uses UC metadata (comments, column names) directly -- missing metadata degrades SQL quality

• v2.0 (Dec 16, 2025) - Genie optimization patterns from production post-mortem

  • Added General Instructions consistency patterns
  • Added ambiguous term definitions
  • Added Metric View vs TVF routing decision table
  • Added TVF syntax guidance
  • Added professional language standards
  • Key Learning: Contradictory rules caused 40% of Genie misrouting

• v1.0 (Jan 2025) - Initial skill based on Genie Space deployment

  • 7-section mandatory structure
  • Benchmark questions with SQL requirement
  • Extended instructions template

Genie Space Notes to Carry Forward

After completing Genie Space configuration, carry these notes to the next worker:

• Genie Space names and JSON paths: List of all configured spaces with their JSON config file paths
• Asset assignments per space: Which Metric Views, TVFs, and Gold Tables are assigned to each space
• Benchmark question counts: Number of benchmark questions per space (minimum 10 required)
• Warehouse assignment: SQL Warehouse ID assigned to each space
• Space IDs (if deployed): Genie Space IDs returned from API creation (needed for idempotent re-deployment)
• Template variables used: Confirm ${catalog} and ${gold_schema} are used for portability

Next Step

After Genie Space JSON configs are generated, proceed to: semantic-layer/04-genie-space-export-import-api/SKILL.md — If deploying via REST API (recommended for cross-environment promotion). Otherwise, proceed to Phase 4 (Asset Bundle Configuration) in the orchestrator.