AI Engineer
hardai-engineer-rag
What is Retrieval-Augmented Generation (RAG) and how do you build it?
Answer
RAG combines retrieval (search) with generation (LLM) to ground answers in your data.
Core steps:
- Chunk documents and create embeddings
- Store in a vector database
- Retrieve top-k relevant chunks
- Prompt the model with retrieved context
Quality depends on chunking, retrieval, and evaluation—not just the LLM.
Related Topics
RAGLLMVector Search