RAG Notes — Personal Knowledge Base With Retrieval That Stays Grounded
A notes + documents app that answers questions with citations and guardrails, optimized for real workflows instead of demo prompts.
Problem Statement
Most “RAG demos” look good until you try to use them daily: missing citations, inconsistent answers, and unclear failure modes. I wanted a knowledge base that makes retrieval quality visible and keeps answers grounded in source text.
Architecture
Ingestion pipeline normalizes documents into chunks with metadata. Embeddings power semantic search; retrieval produces a shortlist with confidence signals. Responses are generated with strict quoting/citation rules and a fallback path when evidence is weak.
Role & Contributions
Designed chunking strategy, metadata schema, and retrieval evaluation approach. Implemented grounded response format and “no-evidence” behavior to reduce hallucinated answers.
Challenges
Balancing recall vs precision, and making low-confidence results obvious to the user. Preventing “helpful but wrong” completions when sources are thin.
Outcomes
A practical RAG workflow: searchable corpus, citation-first answers, and an evaluation-first development loop. (Replace with your real usage metrics when available.)