One of the most critical steps toward autonomous capabilities, where natural language instructions can be successfully converted into executable API calls, is integrating Large Language Models (LLMs) into the ecosystem of the Internet of Drones (IoD). This study introduces an end-to-end LLM-based framework for enhancing real-time drone operation and problem handling in intent recognition, parameter extraction, and ambiguity resolution. It has resorted to a spectrum of methodologies in the form of Retrieval-Augmented Generation (RAG) and customized fine-tuning specific to each domain, towards accurate command interpretation and successful API generation. This paper evaluates the performance of the model on a carefully designed dataset containing 1,500 commands for the different scenarios of IoD with an average BLEU score of 89.6 and a cosine similarity of 0.94. Optimization techniques, such as parallel processing and better query handling, reduced the latency of this system by 15%, with an average query processing time of 0.9 seconds. This work gives considerable importance to the scalability and flexibility of the system, which is quite crucial for applications in domains like disaster response, precision agriculture, and surveillance. Proposed LLM-based framework thus tries to bridge gap between human intent and drone execution for intuitive, reliable, and efficient IoD deployments.
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