How to Build RAG

Overview

A citation-grounded RAG assistant that answers "How do I…?" questions about building RAG systems. The project is intentionally narrow in scope: it focuses on implementation guidance, not general AI theory or chat. Each answer is structured, grounded in retrieved sources, and explicitly cited (article + section + date when available). In addition to the standard question–answer flow, the project supports an Essay mode for presentation-oriented output: short, narrative explanations suited to slides, speaker notes, and high-level overviews of RAG concepts, while remaining grounded in retrieved sources.

Challenge

Builders implementing RAG systems need clear, practical answers to implementation questions—chunking, reranking, hybrid search, evaluation—with traceable citations and guidance on tradeoffs and pitfalls.

Solution

Built a RAG system that answers practical "How do I…?" questions about building RAG (e.g., chunking documents, when reranking helps, hybrid search with metadata filtering, evaluating RAG quality). The assistant delivers structured answers grounded in retrieved sources with explicit citations, aimed at students, engineers, and researchers who want clear steps, tradeoffs, and debugging guidance. Essay mode (presentation-oriented output) supports writing short, presentation-friendly explanations for slides, speaker notes, and high-level overviews; unlike QA mode, it optimizes for narrative flow and clarity while staying grounded in retrieved sources.

Results

• •Structured answers grounded in retrieved sources
• •Explicit citations (article, section, date when available)
• •Focus on implementation guidance: chunking, reranking, hybrid search, evaluation
• •Designed for builders who want clear steps, tradeoffs, pitfalls, and traceable citations
• •Essay mode: presentation-oriented output for slides, speaker notes, and overviews, with narrative flow and clarity while remaining source-grounded