Building RAG Systems That Actually Work

# Building RAG Systems That Actually Work

When we set out to build an AI advisor chatbot for over 10,000 graduate students at UA Little Rock, we didn't realize how quickly "simple retrieval" becomes a systems problem.

The Problem with Naive RAG

Most RAG tutorials show you the happy path: embed your documents, store them in a vector DB, retrieve top-k chunks, feed to LLM. It works beautifully — until it doesn't.

In production, we encountered three failure modes almost immediately:

**1. Semantic mismatch.** Students asking "how do I apply to the CS program" wouldn't match our chunks about "Computer Science MS application requirements" because they used different vocabulary.

**2. Context fragmentation.** When we chunked our faculty handbook at 512 tokens, important context about prerequisites and deadlines ended up in different chunks.

**3. Hallucination on gaps.** When the answer wasn't in our corpus, the LLM would confidently invent policy details.

What We Actually Built

Using LangChain and LangSmith for observability, we implemented a multi-stage hybrid retrieval pipeline combining BM25 keyword search with dense vector retrieval.

Results

After two iterations: response accuracy improved 30%+, fallback rate dropped 40%.