When designing AI systems, choose RAG (Retrieval-Augmented Generation) for lookup-heavy tasks with single data sources requiring fast, low-cost responses, as it is simpler to implement; choose AI agents for tasks requiring actions, calculations, multiple dynamic data sources, or deeper reasoning, as they offer greater capabilities but require more maintenance and are acceptable only when latency is not a critical constraint.
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The Ultimate RAG vs Agent Decision Tree #AIEngineering #DecisionMakingAdded:
Whenever you're designing any AI system, first ask yourself, is this mostly look up? Then if yes, rag is usually sufficient. If it requires action or calculations, lean towards MCV based agentic system. And if the design contains single data source, go for rag.
But if it has multiple and dynamic sources, agent is more suitable. And coming to latency and cost constraints, if you need fast and low-cost response, favors rag. While agents are acceptable only when deeper reasoning or the delay.
And finally, rag is simpler to implement, but agents require more maintenance, but unlock more capability.
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