AI Performance Audit Workflow

Help me set up a performance audit for [web app/API/service]. Current complaints: [symptoms]. Define: the key metrics to measure (TTFB, LCP, API latency, etc.), tools to use for profiling, a testing methodology (what to measure, under what conditions), and target benchmarks based on industry standards for [type of application].

Analyze this performance data and identify bottlenecks: [paste Lighthouse report/profiling data/slow query logs]. For each issue: explain why it's slow, estimate the impact on user experience, rate fix difficulty (easy/medium/hard), and estimate improvement if fixed. Rank by impact-to-effort ratio.

Write optimized code to fix this performance bottleneck: [describe issue with code]. Current implementation: [paste code]. Optimize for: [speed/memory/both]. Show the optimized version with comments explaining each change, expected improvement, and any tradeoffs. Include a before/after benchmark test.

Create a performance audit report. Before metrics: [data]. After metrics: [data]. Changes made: [list]. Format as: executive summary with headline improvement, detailed breakdown per optimization, remaining issues and next steps, and recommendations for ongoing performance monitoring. Include charts comparing before/after.