How RAG Systems Bridge the Gap Between LLMs and Custom Documents

Retrieval-Augmented Generation (RAG) addresses a core limitation of Large Language Models, which can only draw on knowledge from their training data and struggle with domain-specific or proprietary documents. A developer at AI firm Valentius Kryptix built a lightweight RAG pipeline using Python, ChromaDB, and Google's Gemini model to explore this approach. The system uses the all-MiniLM-L6-v2 embedding model to convert both document content and user queries into semantic vectors, enabling meaning-based retrieval even when exact wording differs. ChromaDB stores these vectors, allowing the system to surface conceptually relevant passages before Gemini formulates a response grounded in the source material. A side-by-side test on a Data Structures and Algorithms PDF showed that responses generated with retrieval were more accurate and document-specific compared to those relying solely on the model's general training knowledge.