Custom Chatbot with RAG

This project uses a deliberately small but structured domain to explore a larger idea: how to make language-model outputs more reliable by grounding them in retrieved context.

The chatbot is built around a curated dataset of fictional characters and uses a full RAG pipeline with embeddings, retrieval, and prompt conditioning. The dataset is playful, but the methodological point is serious: retrieval changes the behavior of the model from generic completion to context-bounded reasoning.

Because the underlying data is semantically rich, the system can handle not only question answering but also character comparison, recommendation, and trait-based exploration. That makes it a useful compact example of retrieval-driven NLP design.