AI-Driven Natural Language Query Suggestions
Pranav Salunkhe
• Developed Natural Language Query Generator, to help users get meaningful insights from their data through natural language search powered by LLMs. Presented to Sr. Director Data Engineering, Hong Wang.
• Optimized legacy code by spearheading the use of Retrieval Augumented Generation and Neo4j to deliver context-aware zephyr-7b, and reduced 2000+ lines of code to less than 600.
• Achieved an average Kullback-Leibler divergence score ranging from 9.3 to 11.67, indicating high relevancy of generated prompts.
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Posted Jul 22, 2024
A tool that suggests users natural language searches so they can get meaningful insights from their data powered by LLMs
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Verint
