What is RAG? Retrieval-Augmented Generation explained.

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RAG (Retrieval-Augmented Generation) connects the language capabilities of AI models to your own business data. No hallucinations, but verifiable answers. We explain how it works, when to use it, and what the pitfalls are.

AI Development

You have an internal knowledge system, a customer portal or a business app. You want users to be able to ask questions and get answers that are correct, based on your own data rather than on whatever ChatGPT has picked up from somewhere on the internet.

That is exactly the problem Retrieval-Augmented Generation solves. RAG combines the language capabilities of large language models (LLMs) with the factual accuracy of your own documents, databases and knowledge bases.

In this article we explain what RAG is, how it works technically, when you should use it, and what the alternatives are. No marketing spiel, just a technical explanation for decision-makers and developers.

What exactly is RAG?

RAG stands for Retrieval-Augmented Generation. It is an architectural pattern in which you connect an LLM (such as GPT-4, Claude or Llama) to an external knowledge source. Rather than relying solely on what the model learned during training, the system first retrieves relevant information and supplies it as context when generating an answer.

The concept was introduced in 2020 by researchers at Meta AI in their paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks". Since then it has become the standard approach for business applications that use LLMs.

Core idea An LLM is good at understanding and formulating language. But it only knows what it has seen during training, and that is a snapshot in time. RAG closes that knowledge gap by first searching for relevant documents with each question and passing them along as context.
The three steps of RAG
1
Retrieve — fetching relevant information

The user asks a question. That question is converted into an embedding (a numerical representation) and compared with the embeddings of your documents in a vector database. The most relevant fragments are selected.

2
Augment — building the context

The retrieved document fragments are combined with the original question into a single prompt. The LLM therefore receives not just the question, but also the factual context needed to answer it.

3
Generate — formulating the answer

The LLM generates an answer based on the context. Because the model has concrete source data, the answers are more factual and hallucinations are greatly reduced.

Why RAG rather than just ChatGPT?

A standard LLM has three fundamental limitations for business use:

Sept 2025 GPT-4o's knowledge cut-off — anything after that is unknown
0% Access to your internal documents, CRM or databases
3–27% Hallucination rate on factual questions without context

RAG resolves all three. The system consults current, internal sources with every question, so answers are always up to date, based on your data, and verifiable through source citations.

When RAG is overkill If you only need publicly available information and factual accuracy is not critical (brainstorming, rewriting text, translating), a standard LLM will do. RAG adds value as soon as you need answers that are specific, current and verifiable.
RAG in practice: five applications
Internal knowledge base Employees ask questions in natural language to a chatbot that answers based on internal manuals, policy documents and procedures. Instead of searching through SharePoint or Confluence, they get a concrete answer with a source reference.
Customer service An AI assistant that answers customer questions based on product documentation, FAQs and previous tickets. Because the system only answers based on existing information, incorrect answers are minimised.
Legal and compliance Lawyers and compliance officers search contracts, regulations and case law using natural language queries. The system retrieves relevant passages and produces a summary with references to the exact source and page.
Product catalogue Customers describe what they are looking for in their own words ("a waterproof jacket for mountain hiking under €200"). RAG searches the product database for semantic relevance, not just keywords.
New employee onboarding New colleagues ask questions about processes, tooling and company culture to an AI fed with the staff handbook, Notion pages and onboarding documents. Available 24/7, always patient.
Technical architecture of a RAG system

A RAG pipeline consists of two main components: an indexing pipeline (offline, one-off or periodic) and a query pipeline (real-time, for every question).

Indexing: preparing documents

Before the system can answer questions, documents must be processed:

1
Gathering documents

PDFs, Word documents, web pages, database records, Notion pages — anything containing relevant knowledge is brought in via connectors.

2
Chunking

Large documents are split into smaller fragments (chunks) of typically 200–500 tokens. Chunk size affects quality: too small and context is lost, too large and the prompt is cluttered with irrelevant information.

3
Generating embeddings

Each chunk is converted into a vector — a list of hundreds of numbers that capture the meaning of the text — using an embedding model such as OpenAI text-embedding-3-large or open-source alternatives like BGE-M3.

4
Storing in a vector database

The vectors are stored in a specialised database such as Pinecone, Weaviate, Qdrant or pgvector (a PostgreSQL extension). These databases are optimised for fast similarity search.

Query: answering a question

When a user asks a question, the system goes through the following in milliseconds:

Question → embedding → vector search (top-k chunks) → assembling the prompt → LLM → answer with sources

Most RAG systems also return the source chunks so users can verify the answer. This is crucial for trust and adoption in business environments.

RAG vs. fine-tuning: when do you choose what?

The two most common methods for getting an LLM to work with your own data are RAG and fine-tuning. They do fundamentally different things:

RAG Fine-tuning
How it works Retrieves relevant information with each question Retrains the model with your data
Data freshness Always up to date (you update the sources) Frozen at the moment of training
Costs Low: embedding + vector DB + API calls High: GPU hours for retraining
Hallucinations Greatly reduced through source grounding Less control, may introduce new hallucinations
Source attribution Possible (you know which chunks were used) Not possible (knowledge sits in the weights)
Time to get started Days to weeks Weeks to months
Best for Factual Q&A based on documents Style, tone or domain-specific language
In practice Most business applications start with RAG. You add fine-tuning later if you need specific behaviour or language that RAG does not solve — for example, an AI that writes in your house style. Both techniques can also be combined.
Five pitfalls in RAG implementation
1
Poor chunk strategy

Chunks that begin or end mid-sentence produce fragmented context. Use overlap between chunks and respect document structure (headings, paragraphs).

2
Relying too heavily on semantic search alone

Vector search is powerful but not perfect. Hybrid retrieval, a combination of semantic search and keyword search (BM25), delivers better results in most benchmarks.

3
No evaluation

Without an evaluation framework (RAGAS, LangSmith or manual spot checks), you cannot tell whether your system is improving. Measure retrieval precision, answer faithfulness and relevance.

4
Not cleaning up outdated documents

A vector database that contains outdated versions of documents will give contradictory answers. Build a pipeline that refreshes documents automatically and removes old versions.

5
Ignoring security and access control

If you index HR documents and financial reports, the system must respect who is allowed to see which information. Implement document-level permissions in your retrieval layer.

Tooling and frameworks

The RAG ecosystem has matured in 2025–2026. These are the most widely used tools:

LangChain / LlamaIndex The two dominant Python frameworks for building RAG pipelines. LangChain is broader (agents, chains), while LlamaIndex focuses more specifically on data indexing and retrieval.
Vector databases Pinecone (managed), Weaviate (open-source), Qdrant (open-source, Rust-based), Chroma (lightweight), or pgvector if you already run PostgreSQL. For most projects, pgvector is the pragmatic starting point.
Embedding models OpenAI text-embedding-3-large for maximum quality, or open-source models such as BGE-M3 and Nomic Embed for more control and lower costs. Multilingual models are essential for Dutch content.
Evaluation RAGAS (open-source evaluation framework), LangSmith (from LangChain), or Arize Phoenix. Each offers metrics for retrieval quality, faithfulness and answer relevance.

Implementing RAG in your organisation?

We build AI solutions that work with your own data, from knowledge base chatbots to intelligent search systems. Tell us about your use case.

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