What is RAG?
RAG (Retrieval-Augmented Generation) is an AI architecture that lets a language model retrieve relevant passages from your own documents and use them as context before it writes an answer. The model answers from your current contracts, procedures and product documentation rather than from its general training data, and it can show which source each answer came from.
Definition
RAG (Retrieval-Augmented Generation) is an AI architecture that lets a language model retrieve relevant passages from a chosen document collection, such as a company's own documents, and use them as context before it writes an answer.
How does RAG work?
RAG has two stages. First, your documents are split into chunks, each chunk is converted into a numeric vector by an embedding model, and the vectors are stored in a vector database. When a user asks a question, the question is embedded too, the closest chunks by meaning are retrieved, and they are sent to the large language model together with the question. The model writes its answer from that context.
When should you use RAG?
Anywhere knowledge changes often and answers need a traceable source: internal procedures, HR policies, product catalogues, technical documentation, support archives. There is no need to retrain the model. Add a new PDF, update the index, and the assistant starts using it. If you need to change the model’s tone or output format, fine-tuning can be considered on top.
What to watch for
Documents often contain personal data. Decide early which documents enter the index, where the data is stored and who may see which document, under GDPR or, for data processed in Türkiye, KVKK, Türkiye’s data protection law. Combining semantic search with keyword search usually improves results, especially for product codes and morphologically rich languages such as Turkish. Citing sources in every answer makes hallucinations much easier to spot.
Related terms
- LLM (Large Language Model)An LLM (large language model) is an AI model trained on very large amounts of text that answers questions, summarises, translates and writes by predicting what text should come next.
- EmbeddingAn embedding is a fixed-length array of numbers (a vector) that represents the meaning of a word, sentence, document or image, so that similar content ends up close together.
- Vector databaseA vector database is a database that stores numeric vectors representing content such as text, images or audio and quickly finds the records closest in meaning to a query.
- AI hallucinationAn AI hallucination is output in which a language model presents information, a source or a figure that does not exist, fluently and confidently, as if it were true.
- Fine-tuningFine-tuning is the process of further training a pre-trained AI model on your own examples so that it performs a specific task, tone or output format better.
Frequently asked questions
What is the difference between RAG and fine-tuning?
RAG gives the model knowledge at question time, so when documents change you only update the index. Fine-tuning changes the model's style or behaviour on a specific task through training. For question answering over company knowledge, RAG is usually enough.
Does RAG eliminate hallucinations?
No, but it reduces them considerably. You can require the model to answer only from retrieved sources, cite them, and say it does not know when nothing relevant is found. Answer quality depends heavily on retrieving the right passages.
Does RAG work with Turkish or mixed-language documents?
Yes. Choosing a multilingual embedding model that represents Turkish well, and splitting documents into meaningful chunks, are the two decisions that matter most for retrieval quality.
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