RAG in Production: Retrieval-Augmented Generation Architectures for Enterprise

Why RAG and Not Fine-Tuning

Fine-tuning an LLM is expensive, hard to update, and prone to hallucinations about data not in the training set. RAG (Retrieval-Augmented Generation) solves this by separating knowledge from reasoning: the LLM reasons over dynamically retrieved documents, not static memorization. For enterprise data that changes frequently (policies, manuals, regulations), RAG is the right architecture.

Chunking, Embeddings, and Retrieval

RAG quality depends on retrieval, not the LLM. Incorrect chunking (too large loses precision, too small loses context) is the main cause of low-quality responses. Strategies: sentence-based chunking with overlap, recursive splitting by headers, and parent-child chunking where the relevant chunk is retrieved but the full parent document is sent to the LLM. Hybrid search (dense embeddings + sparse BM25) consistently outperforms dense-only.

Retrieval Evaluation and Hallucination Detection

Retrieval metrics: Recall@k (were relevant documents retrieved?), MRR (is the most relevant one first?), and NDCG (complete ranking quality). For hallucinations, citation grounding verifies that each LLM claim is supported by a retrieved chunk. Claims without support are flagged as unverified. This requires a post-generation step with an evaluator model (a second LLM or lightweight classifier).