ai
RAG (Retrieval-Augmented Generation)
AI architecture that retrieves relevant documents and uses them to generate accurate answers.
Definition
RAG (Retrieval-Augmented Generation) combines a search step with an LLM step: the system retrieves the most relevant documents from a knowledge base, then feeds them to the LLM as context for generating an answer. This solves the LLM's biggest weakness - hallucinating facts - by grounding answers in actual source material. Common applications: customer support bots that read your help docs, internal knowledge assistants, document Q&A. For service businesses, RAG is the practical way to deploy AI on proprietary data without fine-tuning.
In your business
- →Use RAG for any AI that needs to answer from your specific knowledge (policies, docs, history)
- →Quality of retrieval determines quality of answer - clean source data matters more than model choice
- →Test with adversarial questions to find where retrieval breaks