5 ms·

> 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 b

> 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 3mo ago
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.
- spathi_fwiffo 3mo ago
  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 3mo ago
    I don't know how intentional it is / was, but LLMs in general just love to hear themselves talk!
    - arjie 3mo ago
      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 3mo ago
        Thinking models think through talking, don't reveal that talking, then answer by again thinking through talking. It's kinda funny in a way.
        mdgld 3mo ago
        I always expose reasoning traces. How else can you seriously debug?
        girvo 3mo ago
        The closed models aren't giving you the real thinking traces, though.
        joquarky 3mo ago
        Those traces are just summaries of the reasoning.
        alfiedotwtf 3mo ago
        It’s wild the people don’t want to see the thinking
    - jimbokun 3mo ago
- derefr 3mo ago
  > 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.)
  - cma 3mo ago
    Why would auto mode turn off thinking?
    - derefr 3mo ago
      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.")
  - 3mo ago
- Romario77 3mo ago
  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 3mo ago
    > 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.
    - PhilippGille 3mo ago
      That was the case for early models (Llama etc), but they got much better since then. Not perfect, but good enough. This is from Ministral 3 14B, a 2025 model without reasoning, that you can run on your PC: > Write a Haiku involving HackerNews, and the capability of large language models like you to reply in an exact number of words or syllables. Silicon whispers, exact words in code’s embrace— Haiku blooms anew. Across multiple tries it got it wrong a couple times (by ~2 syllables). But syllables are extra tricky (because of how LLMs use tokens) and the point is that for things like "summarize in 5 bullet points" you will mostly get 5 bullet points, maybe 6, but not 10 or 20, and no need for a tool that count bullet points.
- marsven_422 3mo ago
  [dead]
ignoramous 3mo ago
[flagged]
tshaddox 3mo ago
If you conceptualize this as “there is an appropriate amount of brevity for each situation” then it would be expected for a better model to use different amounts of brevity if it gets better at determining the appropriate amount. My view is that popular models by default output wildly excessive amounts of prose for nearly every use case, so if this changes in a new model that’s a pure win.
- daveguy 3mo ago
  The models don't get better, except when a new one is released. Their performance depends solely on the model training before release and how well you curate the context you feed it. That's it. Contrary to popular belief these things are not intelligent.
  - avarun 3mo ago
    This has absolutely nothing to do with the comment you replied to.
  - nullsanity 3mo ago
    [dead]
  - Cycl0ps 3mo ago
    >The models don't get better, except when a new one is released. My brother in Christ this entire thread is talking about the new model that was released
    - daveguy 3mo ago
      It was edited. Original talked about the model learning. Glad they managed to clarify. Because the models are quite literally stupid.
      - dahart 3mo ago
        > the models are quite literally stupid. You’re arguing via reductionism, and failing to explain the outcomes and emergent properties of the “stupid” system. Humans are made of atoms that are quite literally stupid, so by all means, explain our intelligence and why it’s different than LLMs. (I’m not claiming LLMs are intelligent, BTW, I just don’t think your claim helps nor believe that you can fix it.) https://en.wikipedia.org/wiki/Reductionism#Definitions https://en.wikipedia.org/wiki/Reductionism#Definitions
clhodapp 3mo ago
It sure is suspicious that both Anthropic (adaptive thinking) and OpenAI (Avoid generic brevity instructions) both seem to be suggesting that the best way to improve outcomes is to entirely leave it to them to decide how many tokens get used. I mean, it's true that it would be ideal of this stuff did just get figured out optimally behind the API, but there is definitely an incentive on their side to burn more tokens.
- newshackr 3mo ago
  Perhaps the incentive is for variable behavior. When there is low GPU demand, burn more, but reduce when there is contention.
wrs 3mo ago
> could "break" a lot of things for developers who didn't know to make prompt changes after upgrading to 5.6. How does this differ from the other changes in behavior in 5.6 that will also break things? New models always break things.
8note 3mo ago
this is a dependency update. shouldnt you have good testing for that and not deploy a version update when those tests fail?
RossBencina 3mo ago
For sure verbal diarrhea can be a problem. I think there's a difference between a generic instructions e.g. "be brief" and contextual guidance: "I am an experienced software developer with a recent undergraduate degree in pure mathematics. Be terse, I will ask questions if I need clarification."
- msdz 3mo ago
  [dead]
roncesvalles 3mo ago
I think it is widely known by now that instructions to alter the LLM's "tone", things like asking it to adopt a persona ("you are the world's best programmer"), and overly broad directives ("make no mistakes") always gives poor results. Just state directly what you want. If you want something very specific, add more information. "Prompt engineering" is pseudoscience. To put it another way, you will only get the benchmarked performance if you let it talk the way it talks by default. Trying to modify this neuters the model's IQ.
- davedx 3mo ago
  It's a bit more nuanced than that. Earlier models definitely benefited a lot more from prompt engineering. I remember this distinctly from building data pipelines to do things like extract data from PDFs over the last year or two - there are numerous "tricks" like negative prompting, including the right number of examples, massaging the mock data in the JSON examples so it wasn't "too realistic", and so on. I saw how this impacted recall by running evals, so it wasn't pseudoscience. But what has happened is the models have gotten better - which OpenAI is making explicit for some cases in this release. You need that stuff less and less as they become more human and better at inferring what's required implicitly. You still do need to be explicit, and you probably always will, but you don't need as much "engineering" of the way you're asking for things with more recent models.

anticorporate 3mo ago

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.

spathi_fwiffo 3mo ago
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 3mo ago
  I don't know how intentional it is / was, but LLMs in general just love to hear themselves talk!
  - arjie 3mo ago
    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 3mo ago
      Thinking models think through talking, don't reveal that talking, then answer by again thinking through talking. It's kinda funny in a way.
      - mdgld 3mo ago
        I always expose reasoning traces. How else can you seriously debug?
        girvo 3mo ago
        The closed models aren't giving you the real thinking traces, though.
        joquarky 3mo ago
        Those traces are just summaries of the reasoning.
        alfiedotwtf 3mo ago
        It’s wild the people don’t want to see the thinking
  - jimbokun 3mo ago
derefr 3mo ago
> 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.)
- cma 3mo ago
  Why would auto mode turn off thinking?
  - derefr 3mo ago
    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.")
- 3mo ago
Romario77 3mo ago
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 3mo ago
  > 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.
  - PhilippGille 3mo ago
    That was the case for early models (Llama etc), but they got much better since then. Not perfect, but good enough. This is from Ministral 3 14B, a 2025 model without reasoning, that you can run on your PC: > Write a Haiku involving HackerNews, and the capability of large language models like you to reply in an exact number of words or syllables. Silicon whispers, exact words in code’s embrace— Haiku blooms anew. Across multiple tries it got it wrong a couple times (by ~2 syllables). But syllables are extra tricky (because of how LLMs use tokens) and the point is that for things like "summarize in 5 bullet points" you will mostly get 5 bullet points, maybe 6, but not 10 or 20, and no need for a tool that count bullet points.
marsven_422 3mo ago
[dead]

ignoramous 3mo ago

[flagged]

tshaddox 3mo ago

If you conceptualize this as “there is an appropriate amount of brevity for each situation” then it would be expected for a better model to use different amounts of brevity if it gets better at determining the appropriate amount. My view is that popular models by default output wildly excessive amounts of prose for nearly every use case, so if this changes in a new model that’s a pure win.

daveguy 3mo ago
The models don't get better, except when a new one is released. Their performance depends solely on the model training before release and how well you curate the context you feed it. That's it. Contrary to popular belief these things are not intelligent.
- avarun 3mo ago
  This has absolutely nothing to do with the comment you replied to.
- nullsanity 3mo ago
  [dead]
- Cycl0ps 3mo ago
  >The models don't get better, except when a new one is released. My brother in Christ this entire thread is talking about the new model that was released
  - daveguy 3mo ago
    It was edited. Original talked about the model learning. Glad they managed to clarify. Because the models are quite literally stupid.
    - dahart 3mo ago
      > the models are quite literally stupid. You’re arguing via reductionism, and failing to explain the outcomes and emergent properties of the “stupid” system. Humans are made of atoms that are quite literally stupid, so by all means, explain our intelligence and why it’s different than LLMs. (I’m not claiming LLMs are intelligent, BTW, I just don’t think your claim helps nor believe that you can fix it.) https://en.wikipedia.org/wiki/Reductionism#Definitions https://en.wikipedia.org/wiki/Reductionism#Definitions

clhodapp 3mo ago

It sure is suspicious that both Anthropic (adaptive thinking) and OpenAI (Avoid generic brevity instructions) both seem to be suggesting that the best way to improve outcomes is to entirely leave it to them to decide how many tokens get used. I mean, it's true that it would be ideal of this stuff did just get figured out optimally behind the API, but there is definitely an incentive on their side to burn more tokens.

newshackr 3mo ago
Perhaps the incentive is for variable behavior. When there is low GPU demand, burn more, but reduce when there is contention.

wrs 3mo ago

> could "break" a lot of things for developers who didn't know to make prompt changes after upgrading to 5.6. How does this differ from the other changes in behavior in 5.6 that will also break things? New models always break things.

8note 3mo ago

this is a dependency update. shouldnt you have good testing for that and not deploy a version update when those tests fail?

RossBencina 3mo ago

For sure verbal diarrhea can be a problem. I think there's a difference between a generic instructions e.g. "be brief" and contextual guidance: "I am an experienced software developer with a recent undergraduate degree in pure mathematics. Be terse, I will ask questions if I need clarification."

msdz 3mo ago
[dead]

roncesvalles 3mo ago

I think it is widely known by now that instructions to alter the LLM's "tone", things like asking it to adopt a persona ("you are the world's best programmer"), and overly broad directives ("make no mistakes") always gives poor results. Just state directly what you want. If you want something very specific, add more information. "Prompt engineering" is pseudoscience. To put it another way, you will only get the benchmarked performance if you let it talk the way it talks by default. Trying to modify this neuters the model's IQ.

davedx 3mo ago
It's a bit more nuanced than that. Earlier models definitely benefited a lot more from prompt engineering. I remember this distinctly from building data pipelines to do things like extract data from PDFs over the last year or two - there are numerous "tricks" like negative prompting, including the right number of examples, massaging the mock data in the JSON examples so it wasn't "too realistic", and so on. I saw how this impacted recall by running evals, so it wasn't pseudoscience. But what has happened is the models have gotten better - which OpenAI is making explicit for some cases in this release. You need that stuff less and less as they become more human and better at inferring what's required implicitly. You still do need to be explicit, and you probably always will, but you don't need as much "engineering" of the way you're asking for things with more recent models.