What is RAG (Retrieval-Augmented Generation)? - Definition & Meaning
Learn what RAG (Retrieval-Augmented Generation) is, how it enriches AI models with current knowledge, and why RAG is essential for accurate business chatbots.
Definition
RAG (Retrieval-Augmented Generation) is an AI technique that enriches a language model with external knowledge by retrieving relevant information from a knowledge base before generating a response. This ensures AI answers are based on current, business-specific data rather than only the model's training data.
Technical explanation
RAG works in two phases: retrieval and generation. In the retrieval phase, the user query is converted into an embedding vector via an embedding model (e.g., OpenAI text-embedding-3 or an open-source alternative). This vector is compared against pre-indexed documents in a vector database (Pinecone, Weaviate, Chroma, pgvector) using cosine similarity or approximate nearest neighbor (ANN) search algorithms. The most relevant document fragments (chunks) are retrieved. In the generation phase, these chunks are provided as context to the LLM along with the original question in a composed prompt. Crucial aspects include chunking strategies (fixed-size, semantic, recursive), chunk overlap, metadata filtering, re-ranking of search results, and hybrid search combining keyword matching with semantic search results. Advanced RAG patterns include multi-query retrieval, hypothetical document embeddings (HyDE), and agentic RAG with tool usage.
How OpenClaw Installeren applies this
OpenClaw Installeren configures a full RAG pipeline as part of every AI assistant installation. You upload your documents, FAQ lists, and product information, and our system automatically indexes them in a vector database on your VPS. The AI assistant consults this knowledge base for every question, ensuring answers are always based on your current business information.
Practical examples
- An HR chatbot answering employee questions about leave policies by using the employee handbook as a RAG knowledge base, ensuring answers always match current policy.
- A technical support assistant using manuals and release notes as its knowledge base to resolve specific product issues with accurate, up-to-date instructions.
- A legal AI assistant retrieving relevant articles from law books and case law via RAG to answer legal questions with source references.
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