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Mobile App Performance Optimization Guide

Optimize mobile app performance: startup time, memory, battery, network efficiency, list rendering, and smooth 60fps animations.

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What this prompt does

This prompt optimizes a [platform] app for performance against your stated [issues]. It works through ten areas: cold-start time toward a [startup_target]ms target, memory reduction from [current_memory] to [target_memory], battery savings for [battery_features], network efficiency (batching, compression, caching), smooth 60fps animations with jank-cause identification, image loading and caching, list and scroll performance for [list_size] items, background-task optimization, bundle-size reduction from [bundle_size], and profiling setup with [profiling_tools]. It asks for before/after benchmarks, a prioritized action list, and automated performance-regression testing.

The structure works because performance work without measurement is guesswork. By anchoring each area to a number — a startup target, a memory ceiling, a list size — the prompt forces concrete goals rather than vague "make it faster" advice. The insistence on before/after benchmarks and regression tests is what keeps the wins from quietly eroding in the next release, which is the usual fate of one-off optimization passes.

When to use it

  • Users complain about slow cold start, janky scrolling, or the app feeling heavy.
  • You're inheriting an existing app and need a structured place to start optimizing.
  • Memory usage is climbing and you need a target and a plan to hit it.
  • A long list ([list_size] items) stutters and you need rendering fixes.
  • You want profiling set up properly with [profiling_tools] before changing code.
  • You need regression tests so performance gains don't silently disappear later.

Example output

Expect a prioritized action list tied to your [issues], with specific techniques per area — virtualization for the list, caching for images, batching for the network — plus a profiling setup using [profiling_tools] and a before/after benchmark template. The regression-testing section describes how to assert against your [startup_target] and [target_memory] so a future change that regresses them fails automatically.

Pro tips

  • Describe [issues] specifically ("3s cold start, janky scroll on the feed") so the advice targets your real bottlenecks instead of generic tips.
  • Profile with [profiling_tools] before changing anything; optimizing without a baseline means you can't prove the change helped.
  • Set [target_memory] and [startup_target] as defensible goals, not aspirations — a target you measure against is what makes the regression tests meaningful.
  • For the [list_size] list, push for virtualization and stable keys first; most scroll jank comes from rendering offscreen rows or rebuilding too much.
  • Treat [battery_features] like location and background sync as the biggest drains and check whether their frequency can drop without hurting UX.
  • Wire the regression tests into CI so the before/after wins are locked in and the next feature can't quietly undo them.

Frequently Asked Questions

Which platforms does this prompt support?
It adapts to the `[platform]` you set — React Native by default, but the same structure applies to native iOS, Android, or Flutter. The profiling tools and specific techniques shift to match the platform you specify.
Do I need to profile before using the suggestions?
Yes, strongly. The prompt sets up profiling with `[profiling_tools]` and asks for before/after benchmarks because optimizing without a baseline means you can't tell whether a change helped, hurt, or did nothing measurable.
How does it fix janky scrolling on large lists?
It targets list and scroll performance for your `[list_size]` items with techniques like virtualization, stable keys, and reduced per-row work, since most jank on long lists comes from rendering offscreen items or rebuilding too aggressively.
Will the improvements stick after future changes?
That's the point of the automated performance-regression testing it includes. By asserting against your `[startup_target]` and `[target_memory]` in CI, a later change that regresses performance fails the build instead of silently shipping.
Engr Mejba Ahmed

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Engr Mejba Ahmed

AI Developer · Software Engineer

I'm Mejba — I design and ship production AI systems, automations, and full-stack apps. If you want this turned into a working solution for your team, let's talk.

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Engr Mejba Ahmed

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