What is RAG in one sentence?

RAG retrieves the most relevant pieces of your own data and inserts them into a language model's prompt, so the model answers from specific, current sources instead of guessing from training data.

Why use RAG instead of fine-tuning?

RAG keeps knowledge in an external store you can update instantly without retraining, and it can cite sources. Fine-tuning changes the model's behavior and style but is slower to update and harder to attribute.

Does RAG stop hallucinations?

It reduces them by grounding answers in retrieved text, but it does not eliminate them. A model can still misread or ignore the context, so citations and evaluation are essential.

What do I need to build RAG?

A way to chunk and embed your documents, a vector store for similarity search, a retrieval step that fetches top matches, and a language model that generates an answer from those matches.

Is RAG only for chatbots?

No. The same pattern powers internal search, support deflection, document Q&A, contract analysis, and any feature where an answer must be grounded in a specific, changing body of knowledge.

Glossary · Data & AI

What is Retrieval-Augmented Generation (RAG)?

RAG is a pattern that bolts a search step onto a language model: before the model answers, the system retrieves the most relevant passages from your own documents and pastes them into the prompt. The result is an answer grounded in specific, current data the model never saw during training — with sources you can show the user.

The problem it solves

A large language model only knows what it absorbed during training. It has no idea about your internal wiki, last week's policy change, or a customer's contract. Ask it anyway and it will often produce a confident, plausible, wrong answer. RAG fixes this by retrieving the actual relevant text and handing it to the model as context, so the model summarizes and reasons over real sources instead of improvising from memory.

The architecture

A standard RAG pipeline has two phases. Ingestion (done ahead of time): split documents into chunks, turn each chunk into an embedding, and store those in a vector database. Query time: embed the user's question, retrieve the most similar chunks, assemble them into a prompt with the question, and let the model generate an answer. Good systems also return citations so the user can verify the source.

RAG vs. fine-tuning

These are often posed as rivals; they solve different problems. RAG injects knowledge — facts, documents, current data — and lets you update that knowledge instantly by changing the store, with no retraining. Fine-tuning changes behavior — tone, format, domain phrasing, how the model responds — and is the right tool when you need the model to act differently, not just know different facts. Many production systems use both: fine-tuning for style, RAG for knowledge.

Where RAG goes wrong

Most RAG failures are retrieval failures, not model failures. If the retriever returns the wrong chunks, the best model in the world will answer from bad context. Common culprits: chunks too large or too small, an embedding model mismatched to the domain, no metadata filtering, and no re-ranking of results. Another quiet failure is the prompt injection risk — if you retrieve from untrusted content, a malicious document can carry instructions. See prompt injection for how that plays out.

Evaluation matters

Because RAG has moving parts, you cannot eyeball quality from a handful of demos. Serious teams measure retrieval quality (did we fetch the right passages?) separately from answer quality (did the model use them faithfully?). They build a labeled question set, track metrics over time, and re-run them on every change to chunking, embeddings, or prompts. This evaluation discipline is part of treating an AI feature as a real product, the same way MLOps treats a model.

At QUANT LAB

When clients ask for an "AI feature," what they usually need is RAG done carefully. Our AI integration work starts with the unglamorous parts: clean ingestion, sensible chunking, the right embedding model, and an evaluation harness so we can prove the thing actually works before it ships. We also treat retrieved content as untrusted input and design the prompt boundary accordingly. A grounded answer with a citation beats a fluent answer with no source, every time.

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