AI design idea for cutting duplicate logs across services
The problem is duplicate logging across about 10 . An existing AI tool already works for one service at a time: it reads that service, applies preset rules, checks cloud logging data for expensive logs, understands business use, and recommends which logs to keep, shorten, or lower in level. The harder case is a request that moves through several services and leaves similar logs in more than one place.
For example, one service may log that it sent license details, while another logs that it received the same request. Each log can make sense alone, but one may be unnecessary when the full request path is viewed. Loading every into one LLM prompt is not realistic because of s.
A better direction is to analyze each separately, normalize log templates, build a short service summary, and use production log volume plus to understand request paths.
Key points
- The single-service AI tool already finds costly logs and suggests keep, shrink, or downgrade actions.
- The new challenge is finding repeated logs across service boundaries.
- A send log in one service and a receive log in another may be redundant in the full request flow.
- Putting all into one LLM prompt is impractical because of s.
- Short service summaries, normalized log templates, production volume, and can guide better decisions.