RAG Pipeline in Production — From Prototype to Reliable Retrieval-Augmented Generation
Build production-ready RAG systems with semantic chunking, embedding optimization, reranking, citation tracking, and hallucination detection.
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Build production-ready RAG systems with semantic chunking, embedding optimization, reranking, citation tracking, and hallucination detection.
Build comprehensive monitoring for RAG systems tracking retrieval quality, generation speed, user feedback, and cost metrics to detect quality drift in production.
Transform user queries to improve retrieval with rewriting, HyDE, step-back prompting, and multi-hop decomposition techniques that boost RAG accuracy.
Understand why vector similarity ranks poorly, how cross-encoder rerankers fix it, and implement production-grade reranking with latency optimization.
Deploy Node.js, Python, and Go backends on Railway with zero configuration. Manage Postgres, Redis, and services from a unified dashboard.