Comment by babblingfish 2 days ago
The big unlock for me reading this is to think about the order of the output. As in, ask it to produce evidence and indicators before answering a question. Obviously I knew LLMs are a probabilistic auto complete. For some reason, I didn't think to use this for priming.
This is misleading if not wrong. A thinking model doesn’t fundamentally work any different from a non-thinking model. It is still next token prediction, with the same position independence, and still suffers from the same context poisoning issues. It’s just that the “thinking” step injects this instruction to take a moment and consider the situation before acting, as a core system behavior.
But specialized instructions to weigh alternatives still works better as it ends up thinking about thinking, thinking, then making a choice.
I think you are misleading as well. Thinking models do recursively generate the final “best” prompt to get the most accurate output. Unless you are genuinely giving new useful information in the prompt, it is kind of useless to structure the prompt in one way or another because reasoning models can generate intermediate steps that give best output. The evidence on this is clear - benchmarks reveal that thinking models are way more performant.
You're both kind of right. The order is less important for reasoning models, but if you carefully read thinking traces you'll find that the final answer is sometimes not the same as the last intermediary result. On slightly more challenging problems LLMs flip flop quite a bit and ordering the output cleverly can uplift the result. That might stop being true for newer or future models but I iterated quite a bit in this for sonnet 4.
Furthermore, the opposite behavior is very, very bad. Ask it to give you an answer and justify it, it will output a randomish reply and then enter bullshit mode rationalizing it.
Ask it to objectively list pros and cons from a neutral/unbiased perspective and then proclaim an answer, and you’ll get something that is actually thought through.
I typically ask it to start with some short, verbatim quotes of sources it found online (if relevant), as this grounds the context into “real” information, rather than hallucinations. It works fairly well in situations where this is relevant (I recently went through a whole session of setting up Cloudflare Zero Trust for our org, this was very much necessary).
Note that this is not relevant for reasoning models, since they will think about the problem in whatever order it wants to before outputting the answer. Since it can “refer” back to its thinking when outputting the final answer, the output order is less relevant to the correctness. The relative robustness is likely why openai is trying to force reasoning onto everyone.