What Makes a RAG System Actually Reliable
· 6 min read
Most retrieval-augmented generation demos look impressive and fall apart under real usage. The gap is rarely the language model — it's the retrieval layer, the evaluation discipline, and what happens when the retrieved context is wrong or incomplete.
Chunking strategy matters more than teams expect. Splitting documents by fixed token counts ignores structure — a table split across two chunks, or a definition separated from the term it defines, silently degrades answer quality in ways that are hard to spot without dedicated evaluation.
Hybrid retrieval — combining dense vector search with keyword or metadata filtering — consistently outperforms vector search alone for domain-specific corpora, especially where exact terms (product codes, legal clauses, error codes) matter.
Finally, citation isn't a UI nicety. Forcing the system to ground every claim in a retrieved source, and measuring how often it actually does, is the single highest-leverage way to catch silent hallucination before a user does.