Glossary · AI & Agents
RAG (Retrieval-Augmented Generation)
Overview
How RAG works
A RAG system has two stages. First, your documents are split into chunks, converted into embeddings, and stored in a vector database. When a user asks a question, the system searches that database for the most relevant chunks, then passes them to the language model as context alongside the original question.
The model answers using that retrieved context rather than relying solely on what it learned during training. This means it can reference current, company-specific information and stay accurate even when the underlying data changes daily.
Why RAG matters for your business
General-purpose models like OpenAI's GPT or Claude don't know your pricing, policies, or product catalog, and they occasionally invent plausible-sounding answers. RAG closes that gap by anchoring every response to sources you control, which is why it powers reliable internal knowledge assistants, support bots, and search over documentation.
Because you update the knowledge base instead of retraining the model, RAG is cheaper and faster to maintain than fine-tuning, and you can trace each answer back to its source for auditing.
How we build RAG systems
Our team designs RAG development and knowledge assistant projects end to end: ingesting your documents, tuning chunking and embeddings, choosing a vector store such as Pinecone or pgvector, and wiring retrieval into AI agents or n8n automations. With 20+ years and 700+ projects, we focus on measurable accuracy and clean integration with the tools you already use.
Where we use it
Related Zen in Tech services
How our team puts RAG to work in real projects.
FAQ
RAG — common questions
Is RAG better than fine-tuning?
They solve different problems. RAG is best when answers must reflect frequently changing or proprietary facts, while fine-tuning is better for teaching a model a consistent style, format, or specialized task. Many production systems combine both.
Does RAG stop AI hallucinations?
RAG greatly reduces hallucinations by grounding answers in retrieved source text, but it doesn't eliminate them entirely. Quality depends on your data, retrieval accuracy, and guardrails that instruct the model to answer only from provided context.
What do I need to build a RAG system?
At minimum you need a document source, an embedding model, a vector database, and a large language model. Most business deployments also add access controls, monitoring, and evaluation to keep answers accurate over time.
Need RAG done right?
Book a free consultation and we’ll map the fastest, most cost-effective path for your project.
Knowledge hub
From our knowledge hub
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