What Is RAG (Retrieval-Augmented Generation)?
RAG grounds an LLM's answers in your own documents by retrieving relevant passages at query time: how most production GenAI apps avoid making things up.
Retrieval-Augmented Generation (RAG): a technique that grounds a language model's answers in your own documents by retrieving the most relevant passages at query time and passing them to the model, so it answers from your data instead of only its training.
Retrieval-Augmented Generation (RAG) is a technique that grounds a language model’s answers in your own documents by retrieving the most relevant passages at query time and passing them to the model, so it answers from your data instead of only its training.
A RAG system splits your documents into chunks, converts them to embeddings stored in a vector database, and at query time retrieves the closest chunks and feeds them to the model as context. The model then answers using that retrieved evidence, and can cite it.
Why it matters
It is the difference between a chatbot that confidently invents policy and one that answers from your actual handbook, contracts or ticket history, with a citation. For most enterprises, RAG is the safest, cheapest path to a useful GenAI assistant, because it needs no model training and updates the moment your documents do.
How Finzarc thinks about it
We build RAG systems that ship to production, not demos: clean chunking, a reranking step, guardrails against hallucination, and evaluation so you can prove accuracy before go-live. Most start delivering inside three weeks. See what we build.
Related
Want this built into your business instead of just explained? See what we ship or book a 30-minute scope call.
Questions, answered.
What is RAG in simple terms?
RAG (retrieval-augmented generation) lets an AI answer from your own documents by looking up the most relevant passages at question time and using them to write the answer, so it stays grounded in your data instead of guessing.
Is RAG better than fine-tuning?
For most business use cases, yes. RAG is cheaper, updates instantly when your documents change, and can cite its sources. Fine-tuning is better for changing a model's style or teaching a narrow skill. Many production systems use both.
Do I need a vector database for RAG?
Usually yes. A vector database stores the embeddings RAG searches over. Options range from pgvector (inside Postgres you already run) to managed services like Pinecone; the right choice depends on scale and what infrastructure you already have.
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