> Avoid generic brevity instructions

That part is confusing because it's not like they provide an example of how default GPT-5.6 output compares with GPT-5.5 both with default output and prompted for brevity. Whenever I use such prompts, it's usually because I want the model to give me the gist in a few sentences. I'd be stunned if GPT-5.6 was that concise by default. I would think that could "break" a lot of things for developers who didn't know to make prompt changes after upgrading to 5.6. What if you were expecting GPT to be as wordy as it usually is? Then suddenly your output is not wordy enough?

Smells like OpenAI trying its best to stave off financial armageddon for another few months. Then again, I'm not sure why they chose to waste so much output computation on verbal diarrhea all this time up to now.

▲

anticorporate 2 hours ago | parent | next [-]

It seems like the way brevity instructions have changed is mis-aligned with how most people would expect to use them or are currently using them.

Here's the example they give:

> Instead of asking for the shortest possible answer, replace brevity instructions with prioritization:

> Lead with the conclusion. Include the evidence needed to support it, any material caveat, and the next action. Omit secondary detail and repetition.

> Keep all required facts, decisions, caveats, and next steps. Trim introductions, repetition, generic reassurance, and optional background first.

Generally speaking, when I ask for a short answer, I want a short answer because I'm not really willing to read through a bunch of bullshit to get to a summary. Putting the onus back on me to assume what the model will return and write a longer prompt detailing exactly what information I want completely misses the point of why I'm asking for a short answer in the first place.

▲

derefr an hour ago | parent | next [-]

> Lead with conclusion.

I would presume (perhaps falsely?) that an instruction like this would lead to the model presenting a conclusion not supported by the evidence, and potentially backtracking as it then tries to justify said conclusion.

Yes, if deliberation happens, the model should figure out what it wants to say during that phase; but if you're using auto mode, the model is not going to be doing any deliberating half the time. In those cases, the output blathering is the model's only chance for deliberation. It "thinks as it talks", per se.

Given that, I would advise a different approach: let it blather, but then get it to write you a conclusion at the end that the model can guarantee will obviate the need to read any of the blathering.

I.e. advise the model to add an "executive summary" to the end of any non-trivial-in-length response. With some wording to carefully navigate the model between "the summary is itself too long" vs "the summary acts more like clickbait, leaving out necessary detail such that it requires actually reading the blather."

Not sure exactly what that wording would look like. I imagine something like "write your postscript executive summary as if you were a senior CIA intelligence analyst summarizing ground-level reports into a daily digest for the Joint Chiefs of Staff. Take up as little of their time as possible, but ensure that any detail critical to decision-making is retained." (But that phrasing might only be useful if the model is delivering a certain type of response, and actively counter-productive otherwise. This kind of thing is delicate.)

▲

prymitive 42 minutes ago | parent | next [-]

Oh the number of time LLM will, for example, be giving me the list of bugs it found in code, when I ask it for a review, just to decide there’s no big half way through explaining it.

▲

radlad an hour ago | parent | prev | next [-]

I don't expect that would be the case. This is what's called BLUF or Bottom Line Up Front: https://en.wikipedia.org/wiki/BLUF_(communication)

The model will still have read the entirety of the document before composing its response. And I believe that even in auto mode, there are thinking tokens behind the scenes.

▲

cma an hour ago | parent | prev [-]

Why would auto mode turn off thinking?

	▲	derefr an hour ago \| parent [-]
		The "auto" mode is (AFAICT) a per-conversation-turn router. (Presumably via a preliminary pass through a very fast tiny model that spits out an number for how challenging it thinks the next response might be to compute.) On high-challenge turns, the auto mode routes to the "thinking" model. But on low-challenge turns, it routes to the "instant" model. And the "instant" model, by design, has no capacity for deliberation. (If it did, it couldn't guarantee that its responses would begin streaming "instantly.")

▲

spathi_fwiffo 2 hours ago | parent | prev | next [-]

Replace 2 word instruction ('be concise') with a 38 word instruction.

Human can no longer be concise when asking for a few sentences instead of 20 paragraphs of BS they don't want to read when all they want is a summary to verify the general direction of the prompt-work before digging into the details.

such progress!

▲

osigurdson 2 hours ago | parent [-]

I don't know how intentional it is / was, but LLMs in general just love to hear themselves talk!

▲

mr_toad 6 minutes ago | parent | next [-]

> LLMs in general just love to hear themselves talk!

Because that’s what’s in the training set. Reticent humans don’t have blogs.

▲

arjie 2 hours ago | parent | prev | next [-]

They do, and I want to encourage them to do so because they think through talking. What I don’t want to do is spend time reading all that.

We will probably just get reader-side affordances for this like auto-folded justification and introduction sections and so on.

Doubtless some chat interface will add this the way they’ve added reasoning folding.

	▲	bcrosby95 2 hours ago \| parent [-]
		Thinking models think through talking, don't reveal that talking, then answer by again thinking through talking. It's kinda funny in a way.

▲

jimbokun 2 hours ago | parent | prev [-]

Is it just a coincidence that the companies creating them charge by the token?

▲

pizzafeelsright 2 hours ago | parent [-]

The aligned incentive appears to be realigning in favor of the corporation.

Pray they do not realign them further.

There are times I require single word answers. I will use whatever model responds as I desire and at this point those models are just a few.

	▲	minimaxir an hour ago \| parent [-]
		The cost-per-task benchmarks align incentives toward more efficient output and those are the ones gaining steam.

▲

Romario77 an hour ago | parent | prev [-]

I think instead of "be concise" you could tell it how long the answer should be. I.e. give the answer in one paragraph. Or in 10 lines max.

At least before it would listen to instructions like this.

	▲	isityettime an hour ago \| parent [-]
		> At least before it would listen to instructions like this. Would it actually follow them? IME LLMs are incapable of estimating the length of their own output, the total length of the current context, etc. They just make stuff up unless they have external tools that can inspect those things for them.

▲

ignoramous an hour ago | parent | prev [-]

[flagged]