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I would expect the solution is to take the NL question and get GPT to transform it into a SQL (or similar) statement to extract the data. Then another call (or

I would expect the solution is to take the NL question and get GPT to transform it into a SQL (or similar) statement to extract the data. Then another call (or set of calls) to generate "reports" summarizing the data returned by the DB query.

deleted 3y ago
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john2x 3y ago
At which point, maybe a "GUI-interface" will be cheaper to build and maintain in the long run. Now if AI could automagically update inventory data with what's actually physically happening on the lot, that would be cool.
Jtsummers 3y ago
That's a very generous take. But that application would be far more useful than just car inventories (the limited application described) and not trained in the manner described (on inventory data). It would be trained on transforming natural language to SQL (or other) query languages, and the application of that is exactly what we're seeing with code generation applications of LLMs (to the extent they're presently useful).
- wizofaus 3y ago
  Existing LLMs are already pretty good at this, no? The tricky part is mapping however the NL question refers to the various types of data to the actual column names, which is where I'd imagine some prompt engineering (or pretraining) would be necessary.
  - deleted 3y ago
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  - wizofaus 3y ago
    BTW I tried it with ChatGPT 3.5 - with a prompt that roughly described the database schema and a question "I need to know the manufacturer for the vehicle with VIN X7820-A and to confirm whether it has the feature 'rear camera' installed", it came back with SELECT TVehicles.Make, CASE WHEN TVehFeatures.FName = 'rear camera' THEN 'Installed' ELSE 'Not Installed' END AS RearCameraStatus FROM TVehicles JOIN TVehFeatures ON TVehicles.TV_ID = TVehFeatures.TV_ID WHERE TVehicles.VIN = 'X7820-A'; One interesting thing to note - I didn't tell it that "Make" and "Manufacturer" are the same thing. I even went the next level and asked it to write me code to execute the query and generate appropriate HTML output from the results. It didn't quite manage it to handle any possible SQL query (remembering that the query itself has been dynamically generated), but wasn't far off. My description of how the output should look was simply "sleek and modern", and it came up with CSS that could be reasonably said to fill that brief.

deleted 3y ago

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john2x 3y ago

At which point, maybe a "GUI-interface" will be cheaper to build and maintain in the long run. Now if AI could automagically update inventory data with what's actually physically happening on the lot, that would be cool.

Jtsummers 3y ago

That's a very generous take. But that application would be far more useful than just car inventories (the limited application described) and not trained in the manner described (on inventory data). It would be trained on transforming natural language to SQL (or other) query languages, and the application of that is exactly what we're seeing with code generation applications of LLMs (to the extent they're presently useful).

wizofaus 3y ago
Existing LLMs are already pretty good at this, no? The tricky part is mapping however the NL question refers to the various types of data to the actual column names, which is where I'd imagine some prompt engineering (or pretraining) would be necessary.
- deleted 3y ago
  [deleted]
- wizofaus 3y ago
  BTW I tried it with ChatGPT 3.5 - with a prompt that roughly described the database schema and a question "I need to know the manufacturer for the vehicle with VIN X7820-A and to confirm whether it has the feature 'rear camera' installed", it came back with SELECT TVehicles.Make, CASE WHEN TVehFeatures.FName = 'rear camera' THEN 'Installed' ELSE 'Not Installed' END AS RearCameraStatus FROM TVehicles JOIN TVehFeatures ON TVehicles.TV_ID = TVehFeatures.TV_ID WHERE TVehicles.VIN = 'X7820-A'; One interesting thing to note - I didn't tell it that "Make" and "Manufacturer" are the same thing. I even went the next level and asked it to write me code to execute the query and generate appropriate HTML output from the results. It didn't quite manage it to handle any possible SQL query (remembering that the query itself has been dynamically generated), but wasn't far off. My description of how the output should look was simply "sleek and modern", and it came up with CSS that could be reasonably said to fill that brief.