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Llm-Augmented Natural Language Query Generation For Nosql Inventory Management

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Observational analysis demonstrates improved query generation in NoSQL systems, suggesting LLMs can simplify data access for nontechnical users.

Integrating state-of-the-art large language models significantly enhances query performance for inventory management, simplifying processes.
Results show that optimized embedding techniques improve semantic retrieval speed, confirming the system's efficiency.
The proposed system employs transfer learning with transformer-based models to convert natural language into optimized NoSQL queries.
Systematic testing highlights successful few-shot prompting techniques, underscoring the adaptive capabilities of the new querying framework.

Chavan et al. (2025) studied this question.

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View Full Paper

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Authors

Observational analysis demonstrates improved query generation in NoSQL systems, suggesting LLMs can simplify data access for nontechnical users.

Integrating state-of-the-art large language models significantly enhances query performance for inventory management, simplifying processes.
Results show that optimized embedding techniques improve semantic retrieval speed, confirming the system's efficiency.
The proposed system employs transfer learning with transformer-based models to convert natural language into optimized NoSQL queries.
Systematic testing highlights successful few-shot prompting techniques, underscoring the adaptive capabilities of the new querying framework.

Chavan et al. (2025) studied this question.

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