This article proposes a two-phase approach for building a conversational analytics engine, separating deterministic planning from LLM-driven execution to enhance security and reproducibility. The system uses a subject graph to resolve specific user-named entities and injects security and join logic via code rather than relying solely on the LLM. This method aims to overcome common text-to-SQL limitations by ensuring that critical components like joins and security are handled deterministically. AI
IMPACT This approach enhances the reliability and security of LLM-powered data querying by separating deterministic code from LLM execution.
RANK_REASON The article describes a specific technical approach for building a conversational analytics engine, focusing on implementation details rather than a novel model release or broad industry trend.
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