Designing Production-Ready RAG Pipelines: Tackling Latency, Hallucinations, and Cost at Scale
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Build production-grade RAG: slash latency, reduce hallucinations, and cut costs with hybrid retrieval, caching, LLM-as-judge, and smart model routing.
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Retrieval-Augmented Generation (RAG) is an advanced AI system which enhances Large Language Models (LLMs) through real-time knowledge integration from external sources. The technique enables LLMs to deliver responses that are both accurate and relevant to the context by using factual data. Organizations that use LLMs for various applications including customer support chatbots and complex data analysis tools need to develop successful RAG pipelines that scale properly to achieve success.
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