More effective LLM-assisted knob value recommendation for continuous domains

Develop more effective techniques to assist large language models in recommending accurate values for DBMS configuration knobs whose value ranges are continuous, improving reliability and practicality of value selection in automatic configuration debugging.

Background

Andromeda currently uses an LLM-based reasoning approach to identify relevant knobs and propose values, but predicting precise knob values is challenging, particularly when the knobs accept continuous ranges rather than discrete settings.

The authors explicitly call for improved methods to help LLMs recommend values for such knobs, noting the additional difficulty posed by continuous domains.

References

Several questions still remain to be answered. Second, a more effective method should be proposed to assist the LLM in value recommendation for knobs. This is a more challenging problem because the range of values can be continuous.

— Automatic Database Configuration Debugging using Retrieval-Augmented Language Models (2412.07548 - Chen et al., 10 Dec 2024) in Section “Conclusions and Future Work”