Instruction-guided text-to-speech (TTS) systems are among the most intuitive for controllable speech synthesis, but writing descriptive instructions in the form of style prompts is too cumbersome for users to maintain natural conversation rhythm. We introduce TurnStyle, a prompt generation system, that produces style prompts for instruction-guided TTS, leveraging a bimodal dialogue history (speech and text transcripts) and a user script query. To address the unintuitive interface problem, TurnStyle creates a style prompt by conditioning the Llama-3.2-3B-Instruct model on a prefix vector generated from an embedded bimodal representation of a dialogue. Computational experiments with performance measured across standard metrics like BERTscore and ROUGE-L show that TurnStyle’s architecture improved on a comparative baseline model for the understudied task of style prompt generation. The introduced model also consumed over 15 times less energy than the baseline and required less memory, suggesting that the TurnStyle framework may feasibly run on edge devices.