Grounding Opus 4.6 with RAG (retrieval-augmented generation) means you retrieve relevant source passages from your knowledge base and include them in the prompt so the model answers from those sources instead of guessing. This is the most reliable way to build documentation assistants, support bots, and internal “ask our docs” systems, especially when your content changes frequently.
A standard RAG pipeline looks like: chunk documents into passages, generate embeddings for each chunk, store vectors plus metadata (url, title, version, access level), then at query time embed the user’s question and retrieve top-k chunks by similarity with optional metadata filters. Next, assemble a prompt with a “Context” section and a strict instruction: “Answer only using the provided context; if the answer isn’t in context, say you don’t know.” Validate outputs by requiring citations to chunk IDs or by checking that key claims appear in retrieved passages.
For the vector store, use Milvus or managed Zilliz Cloud. Store each chunk with metadata like product, version, and lang so you can prevent version drift. This grounding approach makes long context less necessary, improves factual accuracy, and gives you debug hooks: when an answer is wrong, you can inspect retrieval results and fix chunking, filters, or embeddings.