RAG
Enterprise RAG Architecture
Retrieval quality depends on ingestion, chunking, metadata, ranking, evaluation, provenance, and product feedback loops.
9 min read29 June 2026Ubaith Sherif
Retrieval layer
DocsChunksVectorsSources
Start with ingestion quality
A RAG system is only as reliable as the text it retrieves. Clean documents, metadata, deduplication, and chunk boundaries shape answer quality before the model is called.
Make retrieval testable
Create expected-question sets and inspect whether the retriever returns useful evidence before optimizing prompts.
const evalCase = {
question: "What policy covers refund eligibility?",
expectedSources: ["refund-policy-v3"],
mustInclude: ["eligibility", "time window"]
};Expose provenance
The interface should show sources and uncertainty so users can verify the answer instead of treating the model as an oracle.
Documents
Chunks
Embeddings
Retriever
Reranker
Answer
Key takeaways
- Chunking and metadata are product decisions.
- RAG should be evaluated with real questions.
- Good UI makes provenance visible.