RAG (Retrieval-Augmented Generation)
Retrieval-augmented generation (RAG) gives a language model access to your specific knowledge — documents, databases, wikis — so it answers from your content instead of only its training data. It's the most reliable, cost-effective way to build an AI that actually knows about your business.
What is RAG?
Instead of expensive fine-tuning, RAG retrieves the most relevant pieces of your data at query time and passes them to the model, which grounds its answer in that context and can cite sources. This keeps answers accurate and up to date, and lets you add or change knowledge instantly.
How RAG works
Ingest your data
We load and chunk your documents, then convert them into embeddings.
Store in a vector database
Embeddings are indexed for fast semantic search over your content.
Retrieve relevant context
Each question fetches the most relevant chunks of your knowledge.
Generate a grounded answer
The model answers using that context, with citations back to the source.
What we build with RAG
Knowledge-base assistants
Answer staff or customer questions from your documentation.
Customer support
Resolve tickets using your help content and policies.
Internal semantic search
Search across wikis, tickets, and files by meaning, not keywords.
Document Q&A
Ask questions across contracts, reports, and manuals.
Research assistants
Synthesize answers from large sets of documents.
Product copilots
In-app help grounded in your own documentation.
RAG is a good fit for
Businesses with a lot of proprietary knowledge
Support teams answering repetitive questions
Products needing accurate, up-to-date, cited answers
Anyone who tried a chatbot that made things up
Often built with
What does AI development cost?
Read our AI development cost guide for a full pricing breakdown, including ongoing inference costs.
RAG — frequently asked questions
What is RAG?
RAG (retrieval-augmented generation) is a technique where an AI retrieves relevant information from your own data at query time and uses it to generate an answer. This grounds responses in your content, making them accurate, current, and citable rather than relying only on the model's training.
RAG vs fine-tuning — which should I use?
RAG is usually the better starting point: it's cheaper, faster to build, and lets you update knowledge instantly by changing your data. Fine-tuning changes the model's behaviour and is best for style or narrow tasks. Many production systems use RAG, sometimes with light fine-tuning.
Does RAG stop AI from hallucinating?
It dramatically reduces hallucinations by grounding answers in retrieved facts and providing citations, so users can verify sources. Combined with good retrieval and guardrails, RAG makes AI answers trustworthy enough for real business use.
What do I need to build a RAG system?
Your data, a vector database to store and search embeddings, an embedding model, and a language model to generate answers. We handle the ingestion, retrieval quality, and evaluation so the system returns reliable, relevant answers.
Related AI capabilities
Ready to build with RAG?
Tell us what you want to build and CodersArts Build will scope it into a fixed price and timeline — with evaluation and guardrails built in.