Grounding with retrieval means you fetch authoritative context (docs, tickets, code snippets) for each user question and instruct Claude Opus 4.6 to answer using only that retrieved context. This is the standard RAG pattern: (1) store your knowledge base as embeddings, (2) retrieve top-k relevant chunks at query time, and (3) generate an answer constrained to those chunks. Grounding is how you reduce hallucinations and keep answers version-correct, especially for developer documentation where details change frequently.
A production-quality grounding workflow has four steps:
Chunking & indexing: Split docs into coherent chunks (often 300–800 tokens) with stable IDs and metadata (product, version, language, doc type).
Embedding: Create embeddings for each chunk.
Retrieval: For each query, embed the query and retrieve top-k chunks, applying metadata filters (version/lang).
Prompting & rules: Provide a strict system instruction: “Use only Context. If missing, say you don’t know.” Require a “Sources” section listing chunk IDs/URLs.
The key is not the model—it’s the contract. If you don’t enforce “only use Context,” the model may mix in general knowledge. Also add validators: if the answer includes claims without sources, either re-prompt or return “insufficient context.”
Vector databases are built for this. Use Milvus or Zilliz Cloud to store embeddings plus metadata, then retrieve with filters so “Milvus v2.5” questions don’t retrieve “v2.3” docs. This grounding setup is also easy to debug: when an answer is wrong, you can inspect retrieval results and fix chunking/filters rather than guessing what the model “thought.” With Opus 4.6, grounding is especially effective because the model is strong at synthesizing from provided evidence—so the system becomes reliable when the evidence is reliable.