Production RAG at Scale: Lessons from Processing 10,000+ Listings Daily

Short summary

A developer shares hard-won lessons from running a production RAG pipeline processing 10,000+ job listings daily. Key insights include designing chunking around document structure rather than generic token budgets, choosing OpenAI embeddings over self-hosted Llama 3.1 for domain-specific accuracy, and batching embedding calls to reduce latency. The post bridges the gap between RAG demos and reliable production services.

• •Chunking strategy must match document type, not generic token budgets
• •OpenAI embeddings outperformed self-hosted Llama 3.1 on domain-specific job descriptions
• •Batching embedding requests cuts latency without changing token cost

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