Self-improving eval loops have a known failure mode: they hit 100% on the failing set by writing the test data into the prompt. Verbatim user phrases, scenario IDs, case clauses that never generalize. It looks fixed. Two weeks later a call that was passing breaks.
The version we ship at Cekura scans every prompt edit against transcript-leakage signatures and strips the verbatim quotes before redeploy. A "fix" that hardcodes a customer utterance into the system prompt is not a fix. It is the test data leaking into the model.
The other choice that matters: full-set eval after every iteration, not just the failing subset. The healthcare case study runs a 20-scenario set from 75% to 100% over five iterations, and the regression sweep catches two scenarios the edits broke along the way. A loop that re-ran only the failures would have shipped those regressions.
Before any edit, every failure is classified into one of five categories: Gap, Conflict, Ambiguity, CodeBug, or Upstream. Only Gap and Ambiguity respond to prompt changes. CodeBug needs orchestration changes. Upstream needs a tool, knowledge base, or infra fix. Most manual iteration treats all five as Gap, which is why teams burn five rounds on a problem the prompt cannot solve.
If iteration N fails the same way as N-1, the loop stops editing the prompt entirely and escalates: restructure the flow into named states, add deterministic code-level guards, or move to a stronger model. Endless prompt rewording against a structural limit is the manual-loop default.
Lavish wrote up the seven-phase architecture and the iteration-by-iteration walkthrough in the healthcare case study.
cekura.ai/blogs/self-improvi…
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