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TLDR Designing a RAG system is like architecting a library where books can answer questions directly. You need a way to store knowledge (vector database), understand questions (embedding models), find relevant information (search algorithms), and provide answers (API endpoints). Each […]

TLDR Production RAG pipelines require careful planning around data ingestion, quality control, error handling, and monitoring. Key considerations include handling large document volumes, maintaining system reliability, ensuring data freshness, and scaling to support thousands of users. CustomGPT.ai manages production complexity […]

TLDR Legal document RAG systems transform law firm operations by providing instant access to case law, contracts, regulations, and precedents. CustomGPT.ai’s platform processes legal documents with precision, maintains client confidentiality through SOC-2 compliance, and reduces legal research time by 75%. […]

TLDR Customer support RAG APIs transform help desk operations by providing instant, accurate answers to customer queries based on your actual documentation and knowledge base. CustomGPT.ai’s platform processes support docs, FAQs, and product manuals to deliver 24/7 automated support while […]

TLDR RAG architecture patterns help developers build scalable AI systems that can search through company documents and provide accurate answers. This guide covers the three main patterns – Basic RAG, Advanced RAG, and Agentic RAG – with practical implementation advice. […]