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This seems to be trained(? or referencing) on the specific cars' owner's manual.
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Yeah referencing is the way to go, as even finetuning probably captures style more than concrete facts. I know with large context windows we don't really RAG anymore, but for owner's manual lookup with a smaller model it seems ideal.

Something every LLM user ends up learning is that they're far better used as search and summarization tools than as knowledge databases in themselves.

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It's nothing that tool calling/feeding it the correct information can't solve
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Just a few hours ago I gave ChatGPT my window sticker and the installation manual for a new suspension setup. I asked for new hardware that would typically be replaced during this install, like torque-to-yield bolts and fasteners. I also asked for new oil filters. I got a comprehensive grid of the exact part numbers needed in a nice dense table. sol 5.6 high is my daily driver.
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> sol 5.6 high is my daily driver.

Mine, too.

Did you fact-check the output?

I've found the bot to be particularly bad at stuff like producing accurate lists of parts to replace during automotive projects.

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