RAG (Retrieval-Augmented Generation)
RAG is an AI architecture that combines a language model with a retrieval system, allowing the AI to search your documents and data in real time before generating a response.
What is RAG?
Retrieval-Augmented Generation (RAG) is an architecture for AI systems that addresses one of the core limitations of standard LLMs: their knowledge is frozen at training time. A standard LLM knows what was in its training data, nothing more. It cannot access your internal documents, your CRM, your policies, or anything that happened after its training cutoff.
RAG solves this by adding a retrieval step before generation. When a user submits a query, the system first searches a database of your documents (using vector similarity or keyword search), retrieves the most relevant passages, and provides them as context to the LLM. The LLM then generates a response grounded in your actual content.
Why RAG matters for enterprise AI
For enterprise use cases, RAG is often more practical than fine-tuning. Fine-tuning requires retraining the model when your data changes — expensive, slow, and requires specialized expertise. RAG allows you to update your document database without touching the model. When a policy changes, you update the document. The model adapts instantly.
RAG also provides citations. Because the model's response is grounded in retrieved passages, you can show users exactly which document and which section the answer came from. This is critical for regulated industries where auditability matters.
RAG in practice with Wonka AI
Wonka AI uses RAG to connect your LLM to all your internal data sources — SharePoint documents, Notion wikis, Salesforce records, email threads. When an employee asks a question, the system retrieves relevant content from across your tool stack and generates a response grounded in your actual data.
Frequently asked questions
What's the difference between RAG and fine-tuning?
Fine-tuning modifies the model's weights by training it on your data. RAG keeps the model unchanged but gives it access to your data at query time. RAG is faster to implement, easier to update, and provides citations. Fine-tuning is better for tasks requiring the model to internalize a specific writing style or specialized vocabulary.
Does RAG require storing data in the cloud?
Not necessarily. RAG systems can be deployed entirely on-premise. The document database (vector store) and retrieval infrastructure run within your environment. For enterprise deployments with privacy requirements, fully on-premise RAG is the standard approach.
How accurate is RAG?
RAG accuracy depends on the quality of your document database, the retrieval system configuration, and the LLM quality. Well-implemented RAG systems achieve 85-95% answer accuracy on enterprise knowledge bases. The system should also know when it cannot find a reliable answer rather than hallucinating one.
Your data stays yours. Your AI works for you.
Wonka AI deploys a private LLM inside your infrastructure — connected to your existing tools, processing everything on your servers. No data leaves. No cloud dependency. Full GDPR compliance, out of the box.
Book a demo- Model runs on your servers — nothing reaches a third party
- Connects to your full stack: SharePoint, Salesforce, Slack, Jira and more
- Deployed in weeks, not months
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