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New method optimizes LLM context for enterprise Text-to-SQL tasks

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method to optimize the knowledge-base context provided to large language models (LLMs) for enterprise Text-to-SQL tasks. Instead of feeding the entire business logic, which is infeasible, the approach focuses on constructing context from historical usage data. This involves decomposing queries into DAGs and using a distillation procedure to create reusable SQL reference cards from historical query profiles. Experiments on production queries from a retailer showed significant gains in Abstract Syntax Tree (AST) similarity compared to optimizing the retrieval harness alone. AI

IMPACT This research could improve the efficiency and accuracy of LLMs in understanding and querying complex enterprise databases.

RANK_REASON Academic paper detailing a novel method for optimizing LLM context for Text-to-SQL. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method optimizes LLM context for enterprise Text-to-SQL tasks

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Academic paper detailing a novel method for optimizing LLM context for Text-to-SQL. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Kate Gwimm, Carson Eisenach ·

    Beyond the Harness: End-to-End Optimization of Context Artifacts for Enterprise Text-to-SQL

    arXiv:2608.22830v1 Announce Type: new Abstract: Deploying LLMs for enterprise Text-to-SQL is bottlenecked less by the model than by what context reaches it: business logic spans thousands of tables, and no model can ingest a full catalog at once. We argue that the most effective …

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