How Zoë Ingests Context
Zoë, Zenlytic's AI data analyst, leverages multiple sources of context to understand your organization's data ecosystem, analytical philosophy, and business logic. This document explains the different ways Zoë learns about your data and how to optimize each source for better results.
Context Sources
1. Custom System Prompt Context
You can extend Zoë's default system prompt with domain-specific knowledge that provides high-level organizational context:
Industry Context: Specific business metrics, KPIs, and analytical patterns relevant to your sector
Company Terminology: Internal terms, abbreviations, and naming conventions that differ from standard usage
Business Rules: Unique calculation methods, data interpretation guidelines, or analytical frameworks your organization follows
Organizational Structure: How departments, teams, or business units are organized and how this affects data analysis
Optimization Tips:
Include glossaries of company-specific terms and their definitions
Document any unique business rules or calculation methodologies that Zoë should always follow
Explain organizational hierarchies and how they impact data interpretation and reporting
Provide context about data quality considerations, known limitations, or special handling requirements
2. Fine-Tuning Examples
Zoë learns from examples of successfully answered questions to improve her performance:
Query Patterns: Common ways users ask for similar data when she answers successfully
Field Usage: How different measures and dimensions are typically combined for specific analyses
Response Preferences: The analytical approaches and explanations that work best for your team
Optimization Tips:
Review Zoë's responses and hit the thumbs up button to reinforce desired behavior
Use the admin panel to remove any undesired or incorrectly marked examples
Zoë will automatically prioritize frequently used fields in your workspace when making analytical decisions
3. YAML-Based Views
When using Zenlytic's native semantic layer, Zoë reads directly from your YAML view definitions to understand your data structure (see 4. for dbt Metricflow):
Field Definitions: Views, measures, dimensions, and dimension groups, with field descriptions automatically imported from data warehouse metadata (Snowflake, BigQuery, Databricks, etc.)
Topic Structure: How views join together through topics, including topic-level descriptions that provide analytical context
Access Controls: User permissions that determine what data Zoë can access (she cannot see or reference data that users don't have permission to view)
Descriptions: Both user-facing and AI-specific descriptions using the
description
andzoe_description
attributes (Zoë usesdescription
by default, butzoe_description
takes precedence when both are defined)
Optimization Tips:
Use the
zoe_description
attribute to provide AI-specific context that differs from user-facing descriptionsInclude business logic explanations in measure and dimension descriptions
Document edge cases, calculation nuances, or limitations that Zoë should be aware of
4. dbt MetricFlow Integration (Optional)
When using dbt MetricFlow as your semantic layer (instead of Zenlytic's native ZenML), Zoë automatically ingests context from your dbt project:
Semantic Models: Automatically mapped to Zenlytic views for seamless integration
Measures and Metrics: Both dbt measures and metrics map to Zenlytic measures
Dimensions: Converted to Zenlytic dimensions and dimension groups with appropriate time granularities
Relationships: Join logic and entity relationships are preserved and automatically mapped to topics
Documentation: Model and field descriptions carry over from dbt to provide business context for Zoë
Optimization Tips:
Include rich, business-focused descriptions in your dbt models and fields
Use clear, business-friendly naming conventions that will be intuitive for both, Zoë and your end users
For advanced join logic beyond MetricFlow's capabilities, set
use_default_topics
tofalse
in yourzenlytic_project.yml
file and define custom topics that reference your MetricFlow views
Best Practices for Context Optimization
Rich Descriptions
Always include business context in your field descriptions to help Zoë understand not just what the data is, but how it should be used:
# Good: Provides business context
- name: customer_lifetime_value
field_type: measure
type: sum
sql: ${TABLE}.clv
description: Total revenue expected from a customer over their entire relationship
zoe_description: Use this for retention analysis and customer segmentation. Calculated using predictive modeling on historical purchase patterns.
# Less helpful: Technical only
- name: customer_lifetime_value
field_type: measure
type: sum
sql: ${TABLE}.clv
Clear Topic Organization
Organize your topics with descriptive labels and rich context to help Zoë understand the analytical purpose:
# Good: Descriptive topic with Zoë context
type: topic
label: Customer Analytics
base_view: customers
description: Customer data for retention and value analysis
zoe_description: Primary topic for customer health metrics, churn analysis, and segmentation. Includes predictive CLV calculations and behavioral scoring.
Semantic Field Names
Use field names that clearly indicate their business purpose. While not essential since Zoë has extensive world knowledge, descriptive names benefit both, Zoë and your end users:
monthly_recurring_revenue
instead ofmrr
customer_acquisition_cost
instead ofcac
days_since_last_purchase
instead ofdays_diff
Context Hierarchy
Zoë processes context sources in the following priority order:
Custom system prompt context - Company-specific rules, terminology, and high-level organizational knowledge
Structural relationships - How data connects through topics and joins, including topic-level descriptions
Field and view descriptions - Business context from YAML definitions or dbt documentation at the view, measure, and dimension level
Fine-tuning examples - Patterns from previous successful queries and responses that have been marked as helpful
Understanding this hierarchy helps you strategically place your most critical context in the highest-priority locations for maximum impact on Zoë's analytical performance.
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