Text-to-SQL assistants for streaming engines must handle dialect-specific types, windowing rules, and temporal semantics, including constructs poorly handled by generic prompts. We present Self-Refining Prompt Induction (SRPI), an offline method that converts generation failures into reusable dialect instructions while keeping model weights fixed.
A Refiner revises the Generator's prompt using reference-conditioned feedback from three LLM judges; a sampled regression guard checks earlier successes, and a separate Compressor reduces prompt length. Inference uses the frozen prompt without retrieval or query repair.
We evaluate cross-domain and cross-model transfer on Confluent Cloud Flink SQL using a controlled synthetic workload seeded by 30 expert-written examples, with 335 induction examples and 257 questions from held-out domains.
The same induced prompt improves mean judge scores across seven Generators by 7.1–19.2 percentage points, including an increase from 85.3% to 97.5% for the induction model.
A separate compression study reduces prompt length by 52.7% with a 0.19-percentage-point score decrease.
An assistant using the induced prompt is in early access, with initial user feedback to inform subsequent refinement.