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AI Engineering Student Builds RAG Health Query Pipeline From Scratch

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A first-year Artificial Intelligence Engineering undergraduate is documenting the step-by-step construction of a Retrieval-Augmented Generation (RAG) system as a summer learning project. The student began by testing core components individually, including the Gemini API for LLM calls and sentence-transformers for text embeddings, before combining them into a pipeline. In the second phase, short health topic descriptions covering conditions like diabetes and epilepsy were collected, structured as JSON records, and split into smaller chunks to improve embedding accuracy. The third phase involved setting up a Chroma vector database, storing embedded chunks with source metadata, and successfully running initial retrieval queries against the dataset. The next planned step is connecting retrieved chunks to the LLM so the system can generate source-grounded answers to health questions.

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AI Engineering Student Builds RAG Health Query Pipeline From Scratch · ShortSingh