2 Advantages & Disadvantages of Using a Coding Companion
The integration of large language models (LLMs), such as OpenAI’s ChatGPT, into data engineering workflows has transformed the way developers approach coding tasks. What began as conversational AI tools designed to generate human-like text has evolved into sophisticated coding companions capable of assisting with everything from simple SQL queries to complex data transformations. The real value of these models lies not just in their ability to produce code, but in how they enhance productivity, reduce manual effort, and streamline the iterative development process.
In data engineering, where tasks often involve complex data pipelines, nested JSON structures, and fragile logic, LLMs offer meaningful advantages. They accelerate code generation, streamline repetitive tasks, and enable rapid prototyping with minimal input. However, this efficiency carries risks. LLMs do not possess true understanding of data relationships, system architecture, or business goals. As a result, they can introduce subtle errors or generate confident but incorrect responses. These AI-specific inaccuracies, known as hallucinations, are one of the most important limitations to watch for when integrating LLMs into real-world workflows.