Customizing Retrieval Augmented Generation (RAG) Systems

Retrieval augmented generation (RAG) systems are becoming standard for generative AI applications with large language models (LLMs). They improve LLM performance, reduce hallucinations, and dynamically expand the knowledge base. However, business leaders should consider customizing basic RAG setups to meet specific needs and gain a competitive advantage. Compound AI's modular approach allows additional components to be added to the basic setup for more sophisticated systems tailored to unique use cases. Customization strategies include query classifiers, hybrid retrieval, rankers, reference prediction, and advanced setups like Agentic RAG and GraphRAG.

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