# How do I implement RAG using embed-english-v3.0? Source: https://zilliz.com/ai-faq/how-do-i-implement-rag-using-embedenglishv30 Author: zilliz You implement RAG using embed-english-v3.0 by using it for the **retrieval** part of the pipeline: embed your knowledge base into vectors, retrieve the most relevant chunks for a user query, and then pass those chunks to a generator as context. The core steps are: (1) prepare and chunk your documents, (2) embed each chunk with embed-english-v3.0, (3) store embeddings in a vector database, (4) embed user queries at runtime, (5) retrieve top-k chunks, and (6) assemble a prompt that includes the retrieved context. embed-english-v3.0 is responsible for the “find the right context” step, not the final answer generation. In the ingestion phase, chunking is the make-or-break detail. Split documents by structure (headings, paragraphs) and keep chunk sizes consistent. Store metadata fields that you’ll need later: `doc_id`, `chunk_id`, `title`, `section`, `source_url`, and possibly `access_level` if you have permissions. Embed each chunk and store the vectors in a vector database such as [Milvus](https://milvus.io/) or [Zilliz Cloud](https://zilliz.com/cloud) with a schema that includes a 1024-dimension vector field plus your scalar metadata. Build an index and validate retrieval quality with a test set of queries. The goal is: when you search with a realistic question, the top results should be the chunks that truly contain the answer, not just loosely related text. In the query phase, embed the user’s question with embed-english-v3.0, run a similarity search (often top 5–20), and then post-process results before sending them to the generator. Post-processing typically includes deduping chunks from the same document, merging adjacent chunks when they’re contiguous, and applying metadata filters like product version. Then you build a prompt that includes citations or links in your UI layer (not in the model prompt itself, unless your product requires it) and keep the context size bounded. If you use [Milvus](https://milvus.io/) or [Zilliz Cloud](https://zilliz.com/cloud), you can tune search parameters and filters to improve recall without exploding latency. A well-implemented RAG system is mostly about good chunking, stable embeddings, and disciplined retrieval—not about adding complexity everywhere. For more resources, click here: