Using AI to Make Legacy Systems Legible
summary
Uses AI to turn scattered code, docs, logs and Grafana/Prometheus metrics into a first‑pass map, auto‑generated diagrams and extracted business rules; runs system mapping to trace a cancel‑order flow and pull rules, runs risk scouting to list affected flows and missing tests when refund eligibility changes from 14 to 7 days, and runs decision shaping to compare fixes (increase Kafka consumers versus batching DB updates) before changes go live, plus knowledge sharing to auto‑create support playbooks and reduce discovery time and release surprises.
AI-written summary · about
chapters (7)
- 0:00AI for legacy systems
- 4:32Making legacy systems visible
- 10:09Safe changes in legacy systems
- 16:31Choosing improvements with AI
- 26:23Team learning and knowledge sharing
- 33:11Selecting AI setup
- 37:07Balancing AI and human judgment
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