RAG Systems Explained: When to Use Them and When Not To

What Is RAG?

Retrieval-Augmented Generation (RAG) combines a language model with a retrieval system. Instead of relying solely on the model's training data, RAG fetches relevant documents at inference time and includes them in the prompt context. The model then answers based on retrieved content rather than (or in addition to) what it was trained on.

Why RAG Became Popular

LLMs hallucinate. They confidently state things that are false, especially about recent events or specialized domain knowledge not well-represented in training data. RAG reduces hallucinations by grounding answers in source documents you control. It also makes answers auditable — you can trace a claim back to a specific document chunk.

How a RAG Pipeline Works

1. 01
   Ingestion

   Documents are collected from where they live — a wiki, a document store, a ticketing system — and cleaned of navigation, headers and duplicates.

2. 02
   Chunking

   Each document is split into passages small enough to retrieve precisely and large enough to make sense on their own.

3. 03
   Embedding and indexing

   Every chunk is turned into a vector and stored with its metadata: source, section, date, access rights.

4. 04
   Retrieval

   A question is embedded the same way, and the closest chunks are fetched — often combined with keyword search and a reranking step.

5. 05
   Generation

   The model answers from the retrieved chunks and cites them, so a reader can open the source.

When RAG Works Well

Knowledge bases and documentation Q&A: Answering questions over a company's internal docs, support articles, or product documentation is the sweet spot for RAG. The documents are authoritative, change regularly, and are too large to fit in a single prompt context.

Compliance-sensitive use cases: When you need citations and auditability, RAG lets you show the user exactly which source was used to generate an answer.

Domain-specific content: Medical, legal, or technical content that wasn't well-represented in pretraining data benefits significantly from RAG.

When RAG Doesn't Help

Simple factual questions: If the answer is something the base model already knows well, adding retrieval adds latency and complexity with no benefit.

Creative tasks: Writing, summarization, and generation tasks don't benefit from retrieval unless you specifically want the output to be grounded in source material.

Real-time data needs: RAG is only as fresh as your index. For truly real-time data (live prices, current inventory), you need tool calls or API integrations, not RAG.

Common RAG Failure Modes

A poor chunking strategy is one of the most common causes of bad RAG performance. If your document chunks cut across sentence boundaries or split related information, retrieval quality collapses. Invest time in chunking strategy before optimizing embeddings or retrieval algorithms.

Chunking That Holds Up

• Split on the document's own structure — headings, sections, list items — rather than on a fixed number of characters.
• Keep the context with the chunk. Store the document title and section heading as metadata, so a passage about "the deadline" still knows which policy it belongs to.
• Overlap slightly where a sentence or table would otherwise be cut in two.
• Respect access rights at retrieval time. A chunk from a restricted document must not reach a user who could not open the document itself.

How to Evaluate a RAG System

Build a test set from real questions, each with the document that should answer it. Then measure two things separately: whether retrieval finds the right document among the first few results, and whether the answer is correct and cites it. Retrieval failures are common, and averaging both into one score hides where to fix them. Rerun the same set after every change to chunking, embeddings or prompts. If you are planning a knowledge-base assistant, we design RAG systems around this test set from the start.