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AI calls in shop frontends could lead to excessive LLM usage

A user shared an example of how AI, specifically LLM calls, can be integrated into database queries for a shop frontend. The example shows a query that rates items on a scale of 0-10 based on user-defined criteria, with the LLM performing the rating. This approach results in one LLM call per product for each user query, which the user humorously notes could lead to significant upselling. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Demonstrates a novel, albeit potentially inefficient, method for integrating LLM capabilities directly into database operations for real-time product interaction.

RANK_REASON The item is a social media post sharing a humorous observation about AI integration, not a primary source announcement or significant industry event.

Read on Mastodon — fosstodon.org →

COVERAGE [1]

  1. Mastodon — fosstodon.org TIER_1 · [email protected] ·

    Nice example of # ai calls in databases: In a shop frontend, the select is > Select... <rate item from 0-10 with regards to xyz via llm> as rating .... Order by

    Nice example of # ai calls in databases: In a shop frontend, the select is > Select... <rate item from 0-10 with regards to xyz via llm> as rating .... Order by rating desc That's one LLM call per product, per user query 😱 💶 - This must really cause some upselling! 😉 But okay, go…