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

# Principles

> Core principles for optimizing your analytics agent's performance

## The Goal of Context Engineering

Context engineering optimizes the performance of your analytics agent. Well-engineered context leads to more accurate, reliable, and cost-effective responses.

## Core Performance Metrics

The performance of your analytics agent is measured across three key dimensions:

### Reliability

* **Percentage of questions answered** - How often the agent can provide a response
* **Percentage of correct answers** - How accurate those responses are

### Speed

* **Response time** - How quickly the agent can process and respond to queries

### Costs

* **Token costs** - The computational cost of processing context and generating responses
* **Query execution costs** - The cost of running SQL queries against your data warehouse

<Tip>
  These metrics are interconnected. Improving one often requires balancing trade-offs with others. The goal is to optimize all three simultaneously.
</Tip>

## Core Principles

Context engineering follows the same principles as data engineering:

### Measure

Track your agent's performance across all three metrics:

* Monitor query accuracy and answer rates
* Measure response times
* Track token usage and query execution costs

### Iterate

Continuously improve your context based on real-world usage:

* Identify patterns in failures and gaps
* Add missing context or clarify ambiguities
* Test improvements with sample queries
* Refine based on user feedback

### Optimize

Find the optimal balance for your specific use case:

* **Too little context**: Agent can't answer questions, writes incorrect queries, or needs multiple exploratory queries (increasing costs)
* **Too much context**: Higher token costs, slower responses, and confused answers from processing irrelevant information
* **Optimal balance**: Include all necessary information without exploratory queries, exclude irrelevant schemas, and organize context modularly

## Concrete Rules

### 1. Be Exhaustive and MECE

Your context needs to be **Mutually Exclusive, Collectively Exhaustive** (MECE) so that the agent is reliable:

* **Collectively Exhaustive**: All metrics and data points your users might ask about should be defined in your context
* **Mutually Exclusive**: Each metric should have only one canonical definition—no conflicting definitions across tables or documentation
* **Consistent across tables**: The same metric or data point should mean the same thing wherever it appears, ensuring consistency across your schema

**Why it matters:** Missing metric definitions lead to incomplete or incorrect answers. Conflicting definitions across tables cause inconsistent responses. The agent needs a single source of truth for each metric.

### 2. Balance Token Costs

But not too exhaustive so that it's not too costly in terms of tokens:

* Include only relevant schemas, tables, and documentation
* Avoid redundant or unnecessary information
* Focus on what your users actually need

**Why it matters:** Every token costs money. Including irrelevant context increases costs without improving quality.

### 3. Minimize Query Execution

Provide enough context upfront so that it's not too costly in terms of query execution:

* Provide enough context upfront so the agent doesn't need to explore the schema through multiple queries
* Document relationships and join patterns explicitly
* Include example queries that demonstrate efficient patterns

**Why it matters:** Each exploratory query costs money and time. Well-documented context reduces the need for trial-and-error queries.

### 4. Keep It Modular

Your context should be modular so that it's not too costly in tokens and keeps the agent focused:

* Organize context into logical, domain-based modules
* Structure documentation and rules hierarchically
* Enable the agent to load only relevant pieces of context at a time

**Why it matters:** Modular context allows the agent to read small, focused pieces of information rather than processing everything at once. This reduces token costs and improves focus.

## Next Steps

<Card title="Context Playbook" icon="book" href="/nao-agent/context-engineering/playbook">
  Apply these principles step-by-step with our context engineering playbook.
</Card>

<Card title="Evaluation" icon="flask" href="/nao-agent/context-engineering/evaluation">
  Measure and test your agent's performance with unit tests
</Card>