This paper presents an overview of the Shared Task on Prompt Recovery for Large Language Models (LLMs) in Telugu, organized as part of DravidianLangTech @ ACL 2026. The task focuses on identifying the underlying communicative style of Telugu text excerpts, framed as a nine-class single-label classification problem covering Formal, Informal, Optimistic, Pessimistic, Humorous, Serious, Inspiring, Authoritative, and Persuasive tones. The dataset was constructed by collecting Telugu YouTube comments and generating style-modified variants using an LLM, resulting in 3,000 training instances, 300 validation samples, and 301 test samples. A total of 52 teams registered for the shared task, with 13 teams submitting valid system predictions. Systems explored diverse approaches, including transformer-based fine-tuning (IndicBERT, MuRIL, XLM-R), ensemble and stacking methods, pairwise modeling strategies, curriculum learning, and few-shot large language model prompting. Evaluation was conducted using Macro F1-score as the primary metric. The top-performing system achieved a Macro F1-score of 0.2987. Overall results indicate that Telugu prompt-style recovery remains a challenging problem, particularly due to stylistic overlap and high lexical similarity across classes.