> ## Documentation Index
> Fetch the complete documentation index at: https://dadocs.metazense.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Playbook

> A step-by-step guide to building and maintaining effective context for your analytics agent.

## 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

<Tip>
  Starting small helps you validate your approach before scaling. You can always add more tables later.
</Tip>

**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