One post tagged with "prompt-engineering"

Rachel, a Staff Engineer at a mid-size SaaS company, woke up to a Slack message from the support lead: "Why are half our billing tickets going to the technical team?" She checked the deployment log, nothing shipped in a week. She checked the model configuration, same gpt-4o endpoint, same parameters, same code. No errors in the logs, no latency spikes, no alerts fired. But customer complaints about misrouted tickets had doubled in three weeks. Something was wrong.

This is prompt drift, a slow, invisible degradation in LLM output quality that no dashboard catches until a human notices the downstream effects. Rachel's triage prompt, which classifies support tickets and routes them to the right team, worked perfectly at launch. The team tested it carefully, tuned the wording, validated it against sample tickets, and shipped it with confidence. Three months later, it was failing, and nothing in the monitoring stack surfaced the problem until the support lead noticed a pattern in Slack complaints.