Many languages lack the vast datasets needed to develop high-performing language models, resulting in significant disparities in performance across various linguistic tasks. The novel recursive instruction tuning approach proposed in this research addresses this challenge through an iterative feedback mechanism that enhances task-specific performance for lowresource languages. By applying recursive instructions to Mistral, an open-source language model, the study demonstrates substantial improvements in translation accuracy, syntactic parsing, and other complex linguistic tasks where traditional methods often falter. The recursive framework's ability to iteratively refine instructions based on model output is shown to be highly effective, enabling more robust learning even in data-constrained environments. Through rigorous experimentation, the findings reveal that this technique leads to more adaptable and fluent models, particularly in handling the diverse grammatical and syntactic constructs found in low-resource languages. The research demonstrates the practical benefits of recursive instruction tuning, highlighting its scalability and efficacy across a variety of linguistic tasks and language groups.