No not at all, you got it backwards. This was originally called “chain of thought prompting”, and it basically explained a model on how to reason through a problem before providing an answer.

Because of the nature of how LLMs work — text prediction engines - by putting the explicit reasoning steps first, it improves the likelihood of the final answer (which then is being predicted based on the entire reasoning chain as input) being correct.

> To be honest I thought the 'thinking' was the model being asked 'how did you come up with that' and then it generating a plausible explanation.

This evades an easy yes or no, so:

1. Many consumers believe reasoning-models allow that kind of question to be truthfully-answered, and their belief it reasonable given the marketing going on.

2. Implementers probably do not have the same belief when it comes to the terms mean or what capabilities they imply.

3. Yes, it doesn't actually do what the customer wanted it to do, which is a kind of retrospective introspection of internal thoughts and ideas.

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I advocate looking at everything from a document-generation perspective to cut down on traps and cognitive illusions. The "reasoning" models are a change in the style of document being iteratively-grown by the LLM, as opposed to something more anthropomorphized.

* Default: There's just the spoken dialogue between a Human Customer and Helpful Chatbot.

* "Reasoning": There's the spoken dialogue and a bunch of times the Helpful Chatbot character has an internal monologue. This provides more consistency between iterations, and can be mined by custom tools to call external code and insert results.

If your Human Customer character ask "Why did you say that", the LLM does not engage in a different process than "I have eaten an apple."

The LLM has no memories to consult or hidden goals to contemplate, it's the same process of finding more stuff that fits at the end of the document. Any benefits from a "reasoning model" is the LLM generates much better-looking additions because there's more (hidden) stuff for it to confabulate against.

If you ask an LLM afterward how it arrived at an answer, it might produce a plausible but incorrect explanation. But that's not what the thinking stream is; that's actually part of how it generates the answer.

That's not really how LLMs work at all. I would really recommend checking out something like [1] to get a rough understanding and avoid attributing too much to them.

1. https://medium.com/@eshvargb/the-llm-journey-how-neural-netw...