Imagine effortlessly tapping into a vast library of spectral knowledge, instantly getting answers to your burning questions. That's the promise of a groundbreaking new AI-powered Q&A system designed to revolutionize how researchers work with spectral data. Spectral analysis, a cornerstone of scientific discovery across fields like medicine, food science, and materials engineering, involves identifying substances by their unique light interactions. Traditionally, finding the right spectral information has been a time-consuming, manual process of sifting through countless research papers. This new Q&A system, detailed in a recent research paper, aims to automate this tedious process. It introduces the SDAAP dataset (Spectral Detection and Analysis Based Paper), the first of its kind, containing annotated literature data and knowledge instructions specifically for spectral analysis. Leveraging a Retrieval Augmented Generation (RAG) technique, the system intelligently extracts entities from your question, uses them to pinpoint relevant information within SDAAP, and generates a precise answer. This approach is different from traditional instruction-tuned large language models (LLMs), which often struggle with the nuances and highly specific nature of spectral data. The real-world implications are substantial. Scientists can now readily explore various spectral methods for analyzing apples, polymers, or any other sample of interest, including specifics like preprocessing techniques and machine learning models. Imagine quickly discovering how other researchers predicted sweetness in apples using near-infrared spectroscopy. This system provides not just the answer, but also the relevant scientific papers, bolstering trust and enabling further exploration. Of course, challenges remain. The dataset's current size limits its scope, and the evaluation metrics are still evolving. But the future is bright. Expanding the dataset and improving the AI model's ability to handle nuanced questions promise to unlock even greater potential. This innovative Q&A system could reshape how scientists work with spectral data, accelerating research and fostering new discoveries across many fields.