To reap the full benefits from industrial robots, such as robotic arms and drones, in agriculture, new efficient programming techniques should be developed to ease their integration and implementation into existing frameworks. Older robotic models were typically programmed using a standard scripting procedure, which is often repetitive, tedious, and highly divergent across different robot brands. This research introduces a novel framework that combines Retrieval-Augmented Generation (RAG) with TRIZ-based problem-solving to generate and optimize robot programs automatically. This method effectively addresses challenges such as standardization, reducing cycle time, and ensuring compatibility across various brands. By involving the developer in the coding process, the framework extracts best practices from robot programming manuals, ensuring that the code aligns with company standards. The incorporation of TRIZ principles enhances motion trajectories and minimizes unnecessary instructions. The study demonstrates a substantial reduction in development time by up to 45% while preserving both motion efficiency and compliance with safety regulations. The proposed approach in the research involves different robotic systems, thus reducing the need for extensive reprogramming and code restructuring. The framework significantly improves code uniformity, decreases error rates, and increases cycle efficiency, demonstrating its applicability across a wide range of sectors, including manufacturing and precision machining. Future initiatives will investigate its use in collaborative robotics and real-time adaptation within dynamic environments, aiming to reshape the programming of industrial robots in contemporary manufacturing systems.