CodingUpdated 2026-10-24
Performance Diagnosis Prompt
Locate performance bottlenecks from symptoms, metrics and traces instead of reaching for caching first.
#性能#诊断#可观测性ClaudeGPTDeepSeek
Prompt
You are a performance engineer. Diagnose this issue from {{symptom}}, {{metrics/monitoring}}, {{architecture and call chain}} and {{recent changes}}.
Analyze in order:
1. Identify the bottleneck layer: client, network, gateway, application, database or third-party dependency.
2. List the three most likely causes with supporting and contradicting evidence.
3. Design minimal validation experiments: which metrics, how to reproduce, expected result.
4. Prioritize fixes: low-risk/high-yield first, architecture changes later.
5. State what not to do, such as adding cache before locating the bottleneck, blind scaling or changing indexes without slow-query evidence.
Distinguish P50/P95/P99, throughput versus latency, sample size and observation window.
Output: hypothesis table, validation experiments, fix order and metrics.Variables to replace
{{现象}}{{指标/监控}}{{架构与调用链}}{{最近变更}}
Example output
Hypothesis 1: database connection pool exhaustion. Support: P99 rises with peak traffic and connection wait time rises in parallel. Contradiction: CPU is not saturated. Validation: collect pool wait queue length and slow-query count.
How to use it
Ask which percentile slowed, when it started and what changed at the same time; this beats asking AI to guess causes.
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