RAG stands for retrieval-augmented generation.
In plain language, it means an AI system looks up relevant information first and then uses that retrieved context while producing an answer.
This is one of the main ways teams make AI systems more useful with real business data instead of relying only on the model’s base training.
Why teams use RAG
- internal knowledge assistants
- document search and question answering
- support systems that need current business context
- AI workflows that should answer from company data instead of generic memory
What RAG usually needs
- documents or data to index
- a vector store such as Qdrant
- an application layer such as OpenClaw or a chat UI
- an AI model that can answer using retrieved context
Why it matters in TryDirect
RAG is a good example of why AI work is usually a stack problem, not just a model problem. The AI experiments article shows this clearly in practice.