Label Studio is an open-source data labeling platform that provides flexibility when it comes to labeling data for fine-tuning large language models (LLMs). It enables the preparation of custom training data, the collection and evaluation of responses through human feedback, and has various applications beyond simple QA scenarios. To demonstrate its potential, a simple LLM-powered QA system was built using LangChain, ChromaDB, and OpenAI to answer questions about Label Studio. The system captures user interactions, annotates the data in Label Studio, and measures the quality of the system through feedback loops, allowing for continuous improvement and refinement. This iterative approach showcases the importance of fine-tuning LLMs to meet specific application requirements, particularly in overcoming inherent biases and ensuring domain-specific accuracy, paving a promising way forward for making AI truly impactful in real-world scenarios.