If you're building an LLM-powered application, you'll hit this question quickly: should I use RAG (Retrieval-Augmented Generation) or fine-tune the model? Both approaches customize LLM behavior — but they solve different problems. What Is RAG? RAG retrieves relevant documents at inference time and injects them into the prompt. The model stays unchanged — you're giving it fresh context per query. import anthropic from your_vector_db import search # Chroma, Pinecone, etc. client = anthropic . Anthropic () def rag_answer ( question : str ) -> str : docs = search ( question , top_k = 5 ) context = " \n\n " . join ( docs ) response = client . messages . create ( model = " claude-sonnet-4-6 " , max_tokens = 1024 , messages = [{ " role " : " user " , " content " : f " Context: \n { context } \n\n Question: { question } " }] ) return response . content [ 0 ].…