The exponential growth in the research papers and complex mathematical problems has
posed serious challenges in information retrieval and its review for analysis. This paper
presents "Delineate and Decipher," a platform powered by AI using Retrieval-
Augmented Generation models to solve these challenges. The platform will combine
natural language processing with machine learning techniques to provide context-based
answers from research papers and also solve visual math problems through its interactive
interface. A hybrid system was based on the Gemini model of visual problem-solving in
mathematics, whereas the retrieval models were powered with GROQ. The platform
embeds both documents and mathematical equations into vector spaces, hence
guaranteeing efficiency in document searching and retrieval. Precisely, all this necessary
information has kept in mind while making the system function over huge datasets of
research papers by splitting documents into smaller chunks with strong results based on
accurate/context retrieval. It also allows the user to write mathematical equations visually
and get solutions to them using real-time API with Gemini Flash, hence not just a tool
for doing research analysis but also educational purposes. This work saves time for the
researchers and students who search for some information and solutions of cumbersome
mathematics problems. The proposed platform is powered by state-of-the-art AI
techniques: FAISS for vector storage and Google Generative AI embeddings for
document representation. It provides better access, precision, and interactivity to
academic research and education. Future versions might involve even better language
models and more comprehensive datasets, which have the interactions between users and
scholarly materials and mathematical tools