> ## 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.

# Context Engineering Principles

> Optimize your analytics agent's context for reliability, speed, and cost.

## The Three Dimensions

When building context for your analytics agent, you need to balance three interconnected performance dimensions:

* **Reliability** — Answer rate and accuracy. Can the agent answer questions correctly?
* **Speed** — Response time. How fast does the agent respond?
* **Costs** — Token consumption and query execution costs.

## The Approach

Context engineering follows the same methodology as data engineering:

1. **Measure** — Track accuracy, response times, and token usage across real-world usage patterns
2. **Iterate** — Identify failure patterns and refine context accordingly
3. **Optimize** — Find the right balance between sufficient context and efficiency

## Four Concrete Rules

### Rule 1: MECE (Mutually Exclusive, Collectively Exhaustive)

Your context must be:

* **Collectively Exhaustive** — Cover all possible user queries. If a user can ask about it, the context should support it.
* **Mutually Exclusive** — Each metric has one and only one definition. No conflicting or duplicate information.

This prevents incomplete answers and conflicting information.

### Rule 2: Balance Token Costs

Include only relevant schemas, tables, and documentation while eliminating redundancy. This controls costs without sacrificing quality.

* Remove unused tables and schemas
* Eliminate duplicate definitions
* Keep context focused on what matters

### Rule 3: Minimize Exploration

Provide sufficient upfront documentation to prevent exploratory queries:

* Include explicit relationship documentation between tables
* Provide example query patterns
* Document JOIN paths and common aggregations
* Give the agent enough context so it doesn't need to run discovery queries

### Rule 4: Modularity

Organize context into domain-based logical units:

* Structure content by business domain (marketing, finance, product, etc.)
* Use hierarchical organization
* Allow agents to access focused information pieces rather than processing everything simultaneously
* Load only relevant context for each query

## Next Steps

* **Context Engineering Playbook** — Step-by-step implementation guide
* **Evaluation** — How to test and measure your agent's performance