Skip to main content

​
Getting Started with Context Engineering

This playbook provides a systematic approach to building and maintaining effective context for your analytics agent. Follow these steps in order to ensure a solid foundation and scalable growth.

​
First POC on small, reliable context

Step 1: Add Your Data Context Start with a restricted perimeter of your data warehouse:
  • Maximum 20 tables to begin with
  • Focus on clean, gold, or mart layer tables (avoid raw staging tables)
  • Choose tables that represent core business domains
Starting small helps you validate your approach before scaling. You can always add more tables later.
Step 2: Add Your Documentation Repository Include your documentation sources in context:
  • dbt documentation (schema.yml, docs blocks)
  • Semantic layer definitions
  • Any other relevant documentation repositories
This helps the agent understand business logic, relationships, and data lineage. Step 3: Add Company and Domain Rules Create rules that provide context on:
  • Your company - business context, terminology, conventions
  • Different domains covered by your 20 tables - e.g., sales, marketing, finance, operations
These high-level rules set the foundation for domain-specific understanding. Step 4: Add Sub-Rules for Each Sub-Domain For each sub-domain covered, create detailed sub-rules that include:
  • Business definitions - what key terms mean in your organization
  • Metrics definitions - how metrics are calculated and used
  • List of tables - which tables belong to this domain
  • Relevant docs yaml - specific documentation for this domain
This modular approach makes your context easier to maintain and scale.

​
Measure, test and iterate

Step 5: Create a Set of 20 Key Questions Develop a test suite of 20 key questions that represent:
  • Common user queries
  • Critical business questions
  • Edge cases
  • Different complexity levels
These questions will serve as your quality benchmark throughout the process. Step 6: Test and Iterate Test the chat on your 20 questions:
  • Run all questions through the agent
  • Verify answers are correct and complete
  • Identify gaps in context or understanding
  • Iterate on context - add missing information, clarify ambiguities, refine rules
Repeat until all 20 questions are answered correctly. Step 7: Roll Out to Users Once your test suite passes:
  • Roll out to a small group of users initially
  • Track usage - monitor what questions users are asking
  • Monitor real-life performance using logs of questions and feedback
  • Collect user feedback to identify improvement areas
Step 8: Version Control and Quality Assurance Maintain context quality over time:
  • Version your context using git repositories
  • Run dazense test frequently (e.g., weekly or after major changes)
  • Ensure context quality doesn’t drift as you make updates
  • Set up automated tests in CI/CD pipelines
  • Track test results over time to monitor performance trends

​
Scale

Step 9: Scale Gradually As adoption grows:
  • Extend the number of datasets available in the agent
  • Make documentation and rules modular to support scalability
  • Add new domains incrementally, following the same process
  • Maintain the same quality standards as you expand