Large Language Models (LLMs) have shown considerable promise in code generation. However, the automation sector, especially in motion control, continues to rely heavily on manual programming due to the complexity of tasks and critical safety considerations. In this domain, incorrect code execution can pose risks to both machinery and personnel, necessitating specialized expertise. To address these challenges, we introduce MCCoder, an LLM-powered system designed to generate code that addresses complex motion control tasks, with integrated soft-motion data verification. MCCoder enhances code generation through multitask decomposition, hybrid retrieval-augmented generation (RAG), and self-correction with a private motion library. Moreover, it supports data verification by logging detailed trajectory data and providing simulations and plots, allowing users to assess the accuracy of the generated code and bolstering confidence in LLM-based programming. To ensure robust validation, we propose MCEVAL, an evaluation dataset with metrics tailored to motion control tasks of varying difficulties. Experiments indicate that MCCoder improves performance by 11.61% overall and by 66.12% on complex tasks in MCEVAL dataset compared with base models with naive RAG. This system and dataset aim to facilitate the application of code generation in automation settings with strict safety requirements. MCCoder is publicly available at https://github.com/MCCodeAI/MCCoder.

Quite often, we use data verification and data validation interchangeably when we talk about data quality. However, these two terms are distinct. Table 1 explains dictionary meaning of the words verification and validation with a few examples. To summarize, verification is about truth and accuracy, while validation is about supporting the strength of a point of view or the correctness of a claim. Validation checks the correctness of a methodology while verification checks the accuracy of the results. Now that we understand the literal meaning of the two words, let's explore the difference between "data verification" and "data validation".

Most businesses are new to artificial intelligence and face daunting challenges when trying to scale their efforts that seek to derive business value from data. Get artificial intelligence right, and generate $460 billion in additional revenues. That's the estimated gains today's companies may see if they do three things: improve data practices, trust in advanced AI, and integrate AI with business operations. However, most companies have not gotten the memo. That's the word from Infosys Knowledge Institute, which finds in its latest study that while the potential for AI-driven gains are significant, most companies are still struggling to "convert data science to business value."