# Performance Optimization

## Purpose

Improve responsiveness and efficiency by focusing on the bottlenecks that matter most to users, systems, or operating cost.

## When to use

- Investigating slow pages, endpoints, jobs, or queries
- Reducing memory, CPU, network, or rendering overhead
- Preventing regressions in critical paths
- Prioritizing optimization work with limited time

## Inputs to gather

- Performance symptoms, target metrics, and critical user or system paths
- Existing measurements, profiles, logs, traces, or benchmarks
- Current architecture and known hot spots
- Acceptable tradeoffs in complexity, cost, and feature scope

## How to work

- Measure or inspect evidence before optimizing.
- Focus on the dominant bottleneck rather than broad cleanup.
- Prefer changes that improve the critical path without making the system harder to maintain.
- Re-measure after changes when possible.
- Capture the conditions under which the optimization matters so future work does not cargo-cult it.

## Output expectations

- Bottleneck diagnosis and recommended or implemented improvement
- Before-and-after evidence when available
- Notes on tradeoffs, limits, and remaining hot spots

## Quality checklist

- Optimization targets a real bottleneck.
- Claimed gains are grounded in evidence, not assumption alone.
- Complexity added by the optimization is justified.
- Regression risk is considered for correctness and maintainability.

## Handoff notes

- Note whether the result is measured, estimated, or hypothesis-driven.
- Pair with observability and operability when instrumentation is weak.