RAG is the practical, production-ready way to make LLMs useful for enterprise knowledge: retrieve domain documents, feed them to the LLM at inference, and avoid re-training big models for each update. This post lays out a scalable RAG architecture and the engineering tradeoffs.

RAG components

• Embedding encoder (SentenceTransformers / OpenAI embeddings) → vector DB (Pinecone, Milvus, Weaviate, Qdrant) → retriever policy & chunking → ranker → LLM + prompt orchestration. Hugging Face describes RAG composably; LangChain and Pinecone provide practical stacks.

Chunking & indexing

• Chunk documents intelligently (paragraph or sentence level, with overlap) depending on retrieval granularity. Add metadata (source, timestamp, locale) to support filtering for region (e.g., only UAE policies) and freshness.

Vector DB & scaling

• Choose a vector DB that fits throughput/latency, filtering, and cost needs. Test at expected QPS and vector sizes — some DBs scale better for billions of vectors, others prioritize latency and hybrid searches.

Prompting & hallucination control

• Use explicit system prompts that instruct the LLM to cite sources; implement answer extraction & fallback rules when confidence is low. Evaluate retrieval recall and LLM precision separately. Papers on improving RAG systems highlight query expansion and Focus Mode retrieval for higher precision.

Ops & governance

• Version your indices, track embeddings provenance, set retention policies, and monitor retrieval quality and latency. For UAE customers, add data residency tags and PDPL-compliant retention windows.

RAG provides an evolvable, cost-effective pattern to keep LLM answers grounded. Build in retrieval testing, vector DB load testing, and metadata filters (region/language) to operate reliably across Dubai, UAE and US contexts.

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