InsightAI: Bridging Natural Language and Data Analytics

Abstract

This project introduces an innovative application that
leverages generative AI, specifically pre- trained large language models, for
extracting and interpreting data from large databases, transforming it into
comprehensible insights. The approach involves pre-training the model to
establish a foundational understanding of language and context.
Subsequently, the model is fine-tuned to specialize in database querying,
learning to interpret natural language questions and translating them into
precise database queries. The application further utilizes in-context
learning, allowing the model to adapt and refine its understanding based
on the specific context of database interactions. After retrieving the
relevant data, the application employs generative AI algorithms to produce
coherent, natural language answers. This process converts complex
database information into easily understandable insights, bridging the gap
between intricate data structures and user comprehension. To showcase
this technology, the project applies these techniques to a large, synthetic
dataset created using OpenAI API, simulating various customer surveys
across different product segments and customer categories. For example, a
user could query, “What do gold customers think about our premium
broadband service?” The application would then generate and execute the
appropriate database query, followed by presenting a summarized insight
drawn from the data. This project not only simplifies interactions with
large-scale data but also opens new avenues for advanced data analysis and
informed decision-making. The combination of pre-training, fine-tuning,
and in-context learning harnesses the power of pre-trained language
models, enabling the application to navigate and interpret complex
databases with a high degree of accuracy and efficiency