Scaling Enterprise RAG: Lessons from 10,000+ Pages
How systematic evaluation and recursive chunking reduced hallucinations by 60% in a production knowledge retrieval system.
title: "Scaling Enterprise RAG: Lessons from 10,000+ Pages" date: "2026-01-10T00:00:00Z" excerpt: "How systematic evaluation and recursive chunking reduced hallucinations by 60% in a production knowledge retrieval system." tag: "RAG Architecture" readTime: "9 min" accentColor: "red"
Building Fast, Faithful RAG at Scale
Building a toy RAG (Retrieval-Augmented Generation) app is easy. Building a production RAG pipeline that searches across 10,000+ pages of enterprise documentation and serves precise answers in under two seconds is hard.
At T-Mobile, I built the end-to-end RAG pipeline powering customer support knowledge retrieval. Here are the core architectural decisions that made it successful.
Recursive Chunking over Fixed Splits
Chunking strategy has an outsized impact on retrieval quality — it deserves serious experimentation. Naive fixed-size window splits often cut sentences or concepts in half, destroying semantic meaning before the text is even embedded.
Instead, I implemented recursive chunking. This respects document structure (headings, paragraphs, lists), preserving semantic coherence within chunks. We used 512-token windows with a 50-token overlap, which dramatically improved retrieval relevance.
Metadata Filtering is Critical
Pure semantic search can surface contextually similar but categorically wrong results. For example, a search about "billing policies" might return a policy for the wrong department if the text is semantically close.
Metadata filtering (document type, recency, department) implemented alongside ChromaDB vector indexing ensures retrieval stays within appropriate boundaries before the LLM even sees the context.
Evaluation Driven Development
Subjective quality assessment doesn't scale. To prove the system was working, I built an LLM evaluation framework using RAGAS metrics and LangSmith observability.
By quantitatively measuring faithfulness, relevance, and hallucination scoring on every pipeline change, we were able to systematically reduce factual errors by approximately 60%. Evaluation infrastructure should be built alongside the RAG pipeline, not retrofitted at the end.