Comment by jarulraj | Hacker News Reader

Comment by jarulraj | Hacker News Reader

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Thanks for sharing that observation on customer chatbots.

1. Will that query look like this:

  SELECT LLM("{user_question}", order_info)  
  FROM postgres_data.order_table  
  WHERE user_id = “101”;

2. How will a feature store, like Hopsworks, help in this app?

Shameless self-plug: We are building EvaDB [1], a query engine for shipping fast AI-powered apps with SQL. Would love to exchange notes on such apps if you're up for it!

[1] https://github.com/georgia-tech-db/evadb

Why would your projection be this - SELECT LLM("{user_question}", ?

You can train a small llm on your private data to map the user question to tables in your db.

Then Just select with a limit ( or time bounded). The feature store is just another operational store that could have relevant data for the query.

> You can train a small llm on your private data to map the user question to tables in your db.

Can you? You've personally done this? Deployed it to production at some kind of non trivial scale and it's working well? I'm not aware of any "small llm" that approaches the quality of gpt-3.5.

This is called Text2SQL or NL2SQL, it’s a surprisingly difficult problem even with RAG and GPT4 as soon as the query is non trivial, especially if there are semantic differences between the question and the db schema.

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