"Traditionally, integrating LLMs into SQL workflows for AI-based reasoning of data has been a time-consuming, tedious, and costly affair as it requires data movement, prompt engineering, manual model selection, and parameter tuning, analysts pointed out. The movement of data is typically required due to SQL's inability to understand nuance and meaning of unstructured data, making advanced analysis, such as sentiment analysis or categorization, of customer reviews, support tickets, reports, etc., difficult, said Bradley Shimmin, lead of the data, analytics, and infrastructure practice at The Futurum Group."
"To bypass this challenge, data analysts often had to export data from the warehouse, send it to a data scientist, and await the data scientist to send back enhanced, categorized data suitable for analysis using SQL, Shimmin noted, adding that the new AI functions "can literally collapse that entire workflow into a single query, using standard SQL syntax.""
Integrating LLMs into SQL workflows has historically required significant effort, including moving data, prompt engineering, model selection, and parameter tuning. SQL lacks the ability to interpret nuance and meaning in unstructured data, which hinders advanced tasks like sentiment analysis and categorization of customer reviews, support tickets, and reports. Analysts commonly exported warehouse data to data scientists for enrichment and waited for processed results before performing SQL analysis. New AI functions enable embedding AI-based reasoning directly in SQL, removing the export-and-enrich step and allowing enhanced, categorized analysis via a single standard SQL query.
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