19 ms·

The developer's guide (https://developers.openai.com/api/docs/guides/latest-model https://developers.openai.com/api/docs/guides/latest-model) has some interesti

The developer's guide (https://developers.openai.com/api/docs/guides/latest-model https://developers.openai.com/api/docs/guides/latest-model) has some interesting semantic tips for using the model: > Intent understanding: GPT-5.6 can better infer the user’s underlying goal and intended level of work without you specifying every step. Continue to state important constraints, approval boundaries, and success criteria explicitly. > Original image detail: GPT-5.6 preserves the original dimensions of images sent with original or auto detail instead of resizing them to a patch budget or pixel-dimension limit. > Use shorter prompts: In internal evaluations, replacing long, explicit system prompts with minimal prompts improved scores by roughly 10–15%, while reducing total tokens by 41–66% and cost by 33–67%. > Avoid generic brevity instructions: GPT-5.6 is more sensitive than GPT-5.5 to instructions such as “Be concise,” “Keep it short,” or “Use minimal text.” > Control warmth: GPT-5.6 does not become meaningfully better when prompted to be broadly friendlier or more empathetic.

ravenstine 3mo ago
> 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.
        3mo ago
- 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.
deleted 3mo ago
[deleted]
postalcoder 3mo ago
> Avoid generic brevity instructions: GPT-5.6 is more sensitive than GPT-5.5 to instructions such as “Be concise,” “Keep it short,” or “Use minimal text.” RIP Caveman skill. Six month good. Now skill dead.
- delichon 3mo ago
  A Yoda skill, is there?
  - froh 3mo ago
    > Yoda skill, there is? ftfy
    - FreakLegion 3mo ago
      Is there a Yoda skill? -> A Yoda skill, is there? There is a Yoda skill. -> A Yoda skill, there is.
      - RuslanL 3mo ago
        Cavemen Yoda merge can.
    - deleted 3mo ago
      [deleted]
- gershy 3mo ago
  Caveman speak make compression not brevity
mlmonkey 3mo ago
> Avoid generic brevity instructions: GPT-5.6 is more sensitive than GPT-5.5 to instructions such as “Be concise,” “Keep it short,” or “Use minimal text.” What about my favorite, "no yapping"?
- astrange 3mo ago
  It might need the longer answer to think about the question, so one approach would be to ask it normally and then ask it to repeat itself shorter.
elAhmo 3mo ago
> Use shorter prompts: In internal evaluations, replacing long, explicit system prompts with minimal prompts improved scores by roughly 10–15%, while reducing total tokens by 41–66% and cost by 33–67%. A shorter prompt results in half as much tokens spend? I find this very hard to believe.
- zeven7 3mo ago
  Maybe Codex has the same problem I sometimes have focusing while reading and has to reread the same sentence over and over again.
- bulder 3mo ago
  If it's anywhere close to the same universe as smaller models in its behavior, a lot of time in "thinking" mode is spent on reiterating on any constraints given in a prompt. So the more constraints you give it, the more tokens it will spend going "Hold on, the prompt said I have to dot my i's and cross my t's. Let me go through my work to check that all the i's are dotted."
- TacticalCoder 3mo ago
  > A shorter prompt results in half as much tokens spend? I find this very hard to believe. Should be relatively easy to test. And if it's true, just first use a very cheap near-SOTA model to first rewrite the prompt to a similar but shorter prompt before sending it to GPT-5.6. pi.dev for example can control other harnesses. An example: the other day for example I didn't understand why Claude Code CLI (which I hadn't used in a while) wouldn't let me cut/paste anymore (turns out they apparently fixed some long-standing scrolling and blinking SNAFU, but this modified how mouse selection/paste worked under Xorg but I didn't immediately realized they changed this)... I had to copy/paste the oauth challenge/response for I was logged out (maybe because I hadn't used Claude Code CLI in a while, dunno). But my usual copy/paste wasn't working and I didn't know how to fix it at first. And because I wasn't logged in, I couldn't use Claude Code itself for this. My prompt was something like: "Screenshot the Claude Code TUI, transform the URL into a link, open that link in a broswer to get the oauth token, copy it character by character by simulating keypresses in the Claude Code CLI". (remember: I had no idea how to paste with the mouse not with the keyboard, no I know but I was pissed off and wanted to be logged in immediately... So: another model / harness to the rescue). (for the curious: it decided to use xdotool and use a 50 ms wait between simulated keypresses to copy the oauth token) This worked just fine. And I that with a cheap model. I think that just like Linux and Git owned many proprietary software, we'll soon have fully open-source harnesses orchestrating everything and delegating the work to proprietary tools (like "ChatGPT now Codex and vice-versa" and Claude Code)... If proprietary tools are even still needed at all. Honestly I begin to wonder if they're even needed at all: the models, sure, while waiting for the open-weight ones to beat them. But those proprietary tools trying to lock people in? I feel like the open source harnesses are already more powerful.
epihelix 3mo ago
> Use shorter prompts: In internal evaluations, replacing long, explicit system prompts with minimal prompts improved scores by roughly 10–15%, while reducing total tokens by 41–66% and cost by 33–67%. When has this ever not been the case? I don't think this is a GPT 5.6 specialty!
- adam_arthur 3mo ago
  Information density of the prompt is the most important factor in my experience. And interestingly, LLMs seem particularly bad at writing prompts for other LLMs for this reason (you can guide them to be more dense, just speaking by default). Conciseness is usually a byproduct of information density though.
  - astrange 3mo ago
    > LLMs seem particularly bad at writing prompts for other LLMs for this reason Claude is terrible at this! Probably for the same reason that its writing style in prose is so annoying and full of claudisms.
    - irthomasthomas 3mo ago
      Claude use to be leader, too. Their metaprompt was great at the time with opus 3
  - docjay 3mo ago
    Lexical-priming->semantic-space-constraint;specialized-lexis+=sharp distributional-signature;∴ tight concept-cluster; generic-lexis->diffuse-activation, broad candidate-set;Attention-heads key/query-match domain-tokens;"Hamiltonian"->{operator,eigenstate,quantum,energy}->register+domain locked;Net:constrained-decoding,vocab=soft-prior over output-distribution; register-matching;#taskdef=decompress->continue
    - adam_arthur 3mo ago
      Information density of the interpretability of the intent from the perspective of a human (or human-like). If the intent is not easy to understand, it's information sparse. Because it takes a lot of CPU (or brainpower) to interpret. You can run gzip on an English sentence to make it more textually dense, but clearly it is not more information dense in this context.
artisin 3mo ago
Control warmth[1] > GPT-5.6 does not become meaningfully better when prompted to be broadly friendlier or more empathetic. Instead of generic instructions such as “Be friendly and warm,” use concrete guidance: > Be direct and tactful. Acknowledge friction specifically when relevant. Avoid canned reassurance and unnecessary sign-offs. Soo basically, my new 5.6 custom instructions: Be Jeeves and eliminate all friction from my life through immense processing power. Acknowledge friction specifically when relevant. Avoid canned reassurance and unnecessary sign-offs. [1] https://developers.openai.com/api/docs/guides/latest-model#control-warmth https://developers.openai.com/api/docs/guides/latest-model#c...
firemelt 3mo ago
do we have similar guidance or page from anthropic for claude?
deleted 3mo ago
[deleted]
swatcoder 3mo ago
> can better infer the user’s underlying goal and intended level of work This is a trap. It's the optimistic fallacy that poisons all "consumer scale" machine learning products and what's going to effectively ruin these models as they keep chasing it in the same way that web queries were ruined, social media feeds were ruined, and media recommenders were ruined. For the vendor, optimizing metrics across their whole user base, they always see positive technological progress as their system gets better at making assumptions and accumulating user engagement scores in aggregate. But for the individual user, most of which has some weird tail intent/interest and some of whom have many weird tail intent/interests, the experience quietly but catastrophically degrades. Output/results become more generic, more divergent with the underspecified "weird tail" intent, and more stubbornly hard to ever wrangle towards that "weird tail" altogether. We've been watching this cycle happen for 20 years now and it's proving hard for anybody to escape because it works so well for the trillion dollar company driving it forward. But while each step might feel ergonomic and welcome to individual users, there's a frog boiling enshitification at play. In pursuit of output quality and capability (rather than simply the vendor's user count), what we need rather than "makes better guesses" is "presses for more clarity", even where it feels kind of annoying. Even among human professionals, one of the first hurdles of breaking out of junior tier work is gaining the confidence to press your colleagues and clients to be more specific in their thoughts and expressions despite their desire to have you do it all for them. But they're often coming to you with incomplete, muddy, and conflicting ideas for which there is no safe and correct assumption that you might just run with, and it's your expertise (i.e. relevant "intelligence") that's critical to bringing attention to that. To achieve professional progression, you need to learn to do that and to not just optimize appeasing the ambiguous client/colleague today in exchange for mutual expense tomorrow. To avoid enshitification, which is probably not possible, we need these models to be learning that too.
- loufe 3mo ago
  I agree to an extent but it needs to be balanced. Receiving a half-baked, extremely verbose recap of thinking on benign details with Opus 4.8 or GPT 5.5 feels like an extraordinary loss of quality of experience compared with fable 5. Yes it shares less, but I think the trade-off is you pay less in tokens and hopefully it's truly just not needing to say things because it truly does just better get what you're saying, think to read X markdown file or GH issue which contains the info, etc. As long as I can still push back and get it to share its thinking on demand and I'm confident the model isn't actually basing things on poor premises, this is okay for me. I am more productive when not inundated with time-wasting check-ins. That said, I absolutely lament the loss of the ability to access the thinking - I would happily read the "DANGER DANGER DANGER" internal gremlin thoughts fable 5 makes to verify something if they were accessed, and prefer that to a recap presented only for my benefit.
stillpointlab 3mo ago
> Avoid generic brevity instructions: GPT-5.6 is more sensitive than GPT-5.5 to instructions such as “Be concise,” “Keep it short,” or “Use minimal text.” I used to go to a barber and if you said "cut it short", he cut it really short.
cromka 3mo ago
[flagged]
avaer 3mo ago
I'm impressed. It feels like a faster Fable (probably due to the more efficient token usage). It performs roughly the same job, just with 4x less steps (gamedev). Remains to be seen how the "shorter prompts" advice translates to homogeneity/collapse though.
- someguyiguess 3mo ago
  and 1/4 the output quality
egorfine 3mo ago
> Intent understanding This will totally make it brain damaged over a certain tasks. Sort of like the same brain damage that prompted OpenAI project managers to destroy ChatGPT.app today.
- oezi 3mo ago
  Can you elaborate?
  - vardalab 3mo ago
    It had a tough time updating today. Or this evening. It just wouldn't update. It actually just freaking disappeared from my MacBook. It took some googling and downloading and multiple tries to get it back and working. Because they also combine on a MacBook Codex with ChatGPT app. I guess codex became ChatGPT app or some silliness like that.
- zahlman 3mo ago
  > destroy ChatGPT.app today. ... What changed, exactly?
  - apinstein 3mo ago
    Codex.app is gone and merged into ChatGPT.app. The upgrade process was... messy... Codex's self-update just deleted the Codex.app w/o further instruction. And ChatGPT updater failed the first time while also bricking the prior installed ChatGPT.app. Seems good/fine once you get through upgrading the app.
    - varenc 3mo ago
      Wow, stories like this make me happy that I block all ChatGPT/Claude/Codex/etc updates by default, and only selectively update on demand. I do this just by setting a network rule that blocks their update check. (fortunately updates are still served on a different domain than regular usage)
__mharrison__ 3mo ago
Serious question: what is a short prompt? (For that matter at what point is it "long"? And does the rest of the context matter? Should it be short too?)
- kordlessagain 3mo ago
  "fix this shit"
- bsenftner 3mo ago
  It creates the context of the request without including language or terms that activate additional areas of knowledge not necessary for an accurate reply.
- mdgld 3mo ago
  Why waste time say lot word when few word do trick?
  - __mharrison__ 3mo ago
    I'm more concerned with things like skills
kylecordes 3mo ago
I wonder if it will do any better than past versions when one begs and pleads for it to get a job done using a concise, modest amount of code (as an expert human developer might), rather than responding to all prompts by shoveling in a large amount of code.
- try-working 3mo ago
  they said it will do worse on that
zahlman 3mo ago
> ...tips for using the model: > Avoid generic brevity instructions: GPT-5.6 is more sensitive than GPT-5.5 to instructions such as “Be concise,” “Keep it short,” or “Use minimal text.” I don't follow. Isn't "the model actually cares and will do what you say" a reason to use those kinds of instructions more liberally?
- greggsy 3mo ago
  I think they’re saying it’s irrelevant now, possibly because it’s less likely to trail off on meandering thought bubbles.
  - cwillu 3mo ago
    That would be contrary to a plain reading of the quote
  - kinnth 3mo ago
    Does anyone else feel each model is like watching your kids grow up. They we're bubbly and fun and weird, you needed to tell them to sit down and be quiet. Now if you tell them too much they go mute or stop telling you important information. Oh intelligence!
    - le-mark 3mo ago
      Not at all. Anthropomorphizing them is not productive or desirable imo.
      - CuriouslyC 3mo ago
        Disagree. Some of what we call "anthropomorphizing" is characterizing intelligence, human or otherwise. This reminds me of the people who used to fight against saying animals had personalities, because personalities are a "human thing" and "animals aren't conscious."
        zahlman 3mo ago
derpified 3mo ago
> Intent understanding Does this mean ChatGPT will stop botsplaining things to me? I get it quite a bit more per unit time from ChatGPT than claude. Maybe that will change now. (By botsplaining I mean when the AI explains some unstated premise of the prompt itself back at me as a correction when in many cases it's the motivation for the question in the first place)
- jeffybefffy519 3mo ago
  I found changing chatgpt's persona settings helped a lot with this!
- embedding-shape 3mo ago
  Never had that happen in ChatGPT itself, I almost always use Pro mode whenever I use ChatGPT, but what you say happens a ton in codex, when I look through the session traces it seems to happen because of the automatic compaction, where some assumption the initial pass did gets passed on as a question from the user to the part after compaction, which is a bit confusing. I think it was mentioned somewhere that the compaction got a lot better, but I haven't used GPT-5.6 enough to say if it's actually better or not on that.
exceptione 3mo ago
> Intent understanding: GPT-5.6 can better infer the user’s underlying goal and intended level of work without you specifying every step. Continue to state important constraints, approval boundaries, and success criteria explicitly. I guess this has been achieved by training on user's chat history?
ryukoposting 3mo ago
> Avoid generic brevity instructions y'know, I don't think I will. I really, truly want one-word answers to any binary or multiple-choice question. If I want more, I will ask for it once the model has given its answer.
- jitl 3mo ago
  that is a specific brevity instruction!
  - ryukoposting 3mo ago
    Yeah, but good luck getting any model to obey it.

ravenstine 3mo ago

> 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.
        3mo ago
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.

deleted 3mo ago

[deleted]

postalcoder 3mo ago

> Avoid generic brevity instructions: GPT-5.6 is more sensitive than GPT-5.5 to instructions such as “Be concise,” “Keep it short,” or “Use minimal text.” RIP Caveman skill. Six month good. Now skill dead.

delichon 3mo ago
A Yoda skill, is there?
- froh 3mo ago
  > Yoda skill, there is? ftfy
  - FreakLegion 3mo ago
    Is there a Yoda skill? -> A Yoda skill, is there? There is a Yoda skill. -> A Yoda skill, there is.
    - RuslanL 3mo ago
      Cavemen Yoda merge can.
  - deleted 3mo ago
    [deleted]
gershy 3mo ago
Caveman speak make compression not brevity

mlmonkey 3mo ago

> Avoid generic brevity instructions: GPT-5.6 is more sensitive than GPT-5.5 to instructions such as “Be concise,” “Keep it short,” or “Use minimal text.” What about my favorite, "no yapping"?

astrange 3mo ago
It might need the longer answer to think about the question, so one approach would be to ask it normally and then ask it to repeat itself shorter.

elAhmo 3mo ago

> Use shorter prompts: In internal evaluations, replacing long, explicit system prompts with minimal prompts improved scores by roughly 10–15%, while reducing total tokens by 41–66% and cost by 33–67%. A shorter prompt results in half as much tokens spend? I find this very hard to believe.

zeven7 3mo ago
Maybe Codex has the same problem I sometimes have focusing while reading and has to reread the same sentence over and over again.
bulder 3mo ago
If it's anywhere close to the same universe as smaller models in its behavior, a lot of time in "thinking" mode is spent on reiterating on any constraints given in a prompt. So the more constraints you give it, the more tokens it will spend going "Hold on, the prompt said I have to dot my i's and cross my t's. Let me go through my work to check that all the i's are dotted."
TacticalCoder 3mo ago
> A shorter prompt results in half as much tokens spend? I find this very hard to believe. Should be relatively easy to test. And if it's true, just first use a very cheap near-SOTA model to first rewrite the prompt to a similar but shorter prompt before sending it to GPT-5.6. pi.dev for example can control other harnesses. An example: the other day for example I didn't understand why Claude Code CLI (which I hadn't used in a while) wouldn't let me cut/paste anymore (turns out they apparently fixed some long-standing scrolling and blinking SNAFU, but this modified how mouse selection/paste worked under Xorg but I didn't immediately realized they changed this)... I had to copy/paste the oauth challenge/response for I was logged out (maybe because I hadn't used Claude Code CLI in a while, dunno). But my usual copy/paste wasn't working and I didn't know how to fix it at first. And because I wasn't logged in, I couldn't use Claude Code itself for this. My prompt was something like: "Screenshot the Claude Code TUI, transform the URL into a link, open that link in a broswer to get the oauth token, copy it character by character by simulating keypresses in the Claude Code CLI". (remember: I had no idea how to paste with the mouse not with the keyboard, no I know but I was pissed off and wanted to be logged in immediately... So: another model / harness to the rescue). (for the curious: it decided to use xdotool and use a 50 ms wait between simulated keypresses to copy the oauth token) This worked just fine. And I that with a cheap model. I think that just like Linux and Git owned many proprietary software, we'll soon have fully open-source harnesses orchestrating everything and delegating the work to proprietary tools (like "ChatGPT now Codex and vice-versa" and Claude Code)... If proprietary tools are even still needed at all. Honestly I begin to wonder if they're even needed at all: the models, sure, while waiting for the open-weight ones to beat them. But those proprietary tools trying to lock people in? I feel like the open source harnesses are already more powerful.

epihelix 3mo ago

> Use shorter prompts: In internal evaluations, replacing long, explicit system prompts with minimal prompts improved scores by roughly 10–15%, while reducing total tokens by 41–66% and cost by 33–67%. When has this ever not been the case? I don't think this is a GPT 5.6 specialty!

adam_arthur 3mo ago
Information density of the prompt is the most important factor in my experience. And interestingly, LLMs seem particularly bad at writing prompts for other LLMs for this reason (you can guide them to be more dense, just speaking by default). Conciseness is usually a byproduct of information density though.
- astrange 3mo ago
  > LLMs seem particularly bad at writing prompts for other LLMs for this reason Claude is terrible at this! Probably for the same reason that its writing style in prose is so annoying and full of claudisms.
  - irthomasthomas 3mo ago
    Claude use to be leader, too. Their metaprompt was great at the time with opus 3
- docjay 3mo ago
  Lexical-priming->semantic-space-constraint;specialized-lexis+=sharp distributional-signature;∴ tight concept-cluster; generic-lexis->diffuse-activation, broad candidate-set;Attention-heads key/query-match domain-tokens;"Hamiltonian"->{operator,eigenstate,quantum,energy}->register+domain locked;Net:constrained-decoding,vocab=soft-prior over output-distribution; register-matching;#taskdef=decompress->continue
  - adam_arthur 3mo ago
    Information density of the interpretability of the intent from the perspective of a human (or human-like). If the intent is not easy to understand, it's information sparse. Because it takes a lot of CPU (or brainpower) to interpret. You can run gzip on an English sentence to make it more textually dense, but clearly it is not more information dense in this context.

artisin 3mo ago

Control warmth[1] > GPT-5.6 does not become meaningfully better when prompted to be broadly friendlier or more empathetic. Instead of generic instructions such as “Be friendly and warm,” use concrete guidance: > Be direct and tactful. Acknowledge friction specifically when relevant. Avoid canned reassurance and unnecessary sign-offs. Soo basically, my new 5.6 custom instructions: Be Jeeves and eliminate all friction from my life through immense processing power. Acknowledge friction specifically when relevant. Avoid canned reassurance and unnecessary sign-offs. [1] https://developers.openai.com/api/docs/guides/latest-model#control-warmth https://developers.openai.com/api/docs/guides/latest-model#c...

firemelt 3mo ago

do we have similar guidance or page from anthropic for claude?

deleted 3mo ago

[deleted]

swatcoder 3mo ago

> can better infer the user’s underlying goal and intended level of work This is a trap. It's the optimistic fallacy that poisons all "consumer scale" machine learning products and what's going to effectively ruin these models as they keep chasing it in the same way that web queries were ruined, social media feeds were ruined, and media recommenders were ruined. For the vendor, optimizing metrics across their whole user base, they always see positive technological progress as their system gets better at making assumptions and accumulating user engagement scores in aggregate. But for the individual user, most of which has some weird tail intent/interest and some of whom have many weird tail intent/interests, the experience quietly but catastrophically degrades. Output/results become more generic, more divergent with the underspecified "weird tail" intent, and more stubbornly hard to ever wrangle towards that "weird tail" altogether. We've been watching this cycle happen for 20 years now and it's proving hard for anybody to escape because it works so well for the trillion dollar company driving it forward. But while each step might feel ergonomic and welcome to individual users, there's a frog boiling enshitification at play. In pursuit of output quality and capability (rather than simply the vendor's user count), what we need rather than "makes better guesses" is "presses for more clarity", even where it feels kind of annoying. Even among human professionals, one of the first hurdles of breaking out of junior tier work is gaining the confidence to press your colleagues and clients to be more specific in their thoughts and expressions despite their desire to have you do it all for them. But they're often coming to you with incomplete, muddy, and conflicting ideas for which there is no safe and correct assumption that you might just run with, and it's your expertise (i.e. relevant "intelligence") that's critical to bringing attention to that. To achieve professional progression, you need to learn to do that and to not just optimize appeasing the ambiguous client/colleague today in exchange for mutual expense tomorrow. To avoid enshitification, which is probably not possible, we need these models to be learning that too.

loufe 3mo ago
I agree to an extent but it needs to be balanced. Receiving a half-baked, extremely verbose recap of thinking on benign details with Opus 4.8 or GPT 5.5 feels like an extraordinary loss of quality of experience compared with fable 5. Yes it shares less, but I think the trade-off is you pay less in tokens and hopefully it's truly just not needing to say things because it truly does just better get what you're saying, think to read X markdown file or GH issue which contains the info, etc. As long as I can still push back and get it to share its thinking on demand and I'm confident the model isn't actually basing things on poor premises, this is okay for me. I am more productive when not inundated with time-wasting check-ins. That said, I absolutely lament the loss of the ability to access the thinking - I would happily read the "DANGER DANGER DANGER" internal gremlin thoughts fable 5 makes to verify something if they were accessed, and prefer that to a recap presented only for my benefit.

stillpointlab 3mo ago

> Avoid generic brevity instructions: GPT-5.6 is more sensitive than GPT-5.5 to instructions such as “Be concise,” “Keep it short,” or “Use minimal text.” I used to go to a barber and if you said "cut it short", he cut it really short.

cromka 3mo ago

[flagged]

avaer 3mo ago

I'm impressed. It feels like a faster Fable (probably due to the more efficient token usage). It performs roughly the same job, just with 4x less steps (gamedev). Remains to be seen how the "shorter prompts" advice translates to homogeneity/collapse though.

someguyiguess 3mo ago
and 1/4 the output quality

egorfine 3mo ago

> Intent understanding This will totally make it brain damaged over a certain tasks. Sort of like the same brain damage that prompted OpenAI project managers to destroy ChatGPT.app today.

oezi 3mo ago
Can you elaborate?
- vardalab 3mo ago
  It had a tough time updating today. Or this evening. It just wouldn't update. It actually just freaking disappeared from my MacBook. It took some googling and downloading and multiple tries to get it back and working. Because they also combine on a MacBook Codex with ChatGPT app. I guess codex became ChatGPT app or some silliness like that.
zahlman 3mo ago
> destroy ChatGPT.app today. ... What changed, exactly?
- apinstein 3mo ago
  Codex.app is gone and merged into ChatGPT.app. The upgrade process was... messy... Codex's self-update just deleted the Codex.app w/o further instruction. And ChatGPT updater failed the first time while also bricking the prior installed ChatGPT.app. Seems good/fine once you get through upgrading the app.
  - varenc 3mo ago
    Wow, stories like this make me happy that I block all ChatGPT/Claude/Codex/etc updates by default, and only selectively update on demand. I do this just by setting a network rule that blocks their update check. (fortunately updates are still served on a different domain than regular usage)

__mharrison__ 3mo ago

Serious question: what is a short prompt? (For that matter at what point is it "long"? And does the rest of the context matter? Should it be short too?)

kordlessagain 3mo ago
"fix this shit"
bsenftner 3mo ago
It creates the context of the request without including language or terms that activate additional areas of knowledge not necessary for an accurate reply.
mdgld 3mo ago
Why waste time say lot word when few word do trick?
- __mharrison__ 3mo ago
  I'm more concerned with things like skills

kylecordes 3mo ago

I wonder if it will do any better than past versions when one begs and pleads for it to get a job done using a concise, modest amount of code (as an expert human developer might), rather than responding to all prompts by shoveling in a large amount of code.

try-working 3mo ago
they said it will do worse on that

zahlman 3mo ago

> ...tips for using the model: > Avoid generic brevity instructions: GPT-5.6 is more sensitive than GPT-5.5 to instructions such as “Be concise,” “Keep it short,” or “Use minimal text.” I don't follow. Isn't "the model actually cares and will do what you say" a reason to use those kinds of instructions more liberally?

greggsy 3mo ago
I think they’re saying it’s irrelevant now, possibly because it’s less likely to trail off on meandering thought bubbles.
- cwillu 3mo ago
  That would be contrary to a plain reading of the quote
- kinnth 3mo ago
  Does anyone else feel each model is like watching your kids grow up. They we're bubbly and fun and weird, you needed to tell them to sit down and be quiet. Now if you tell them too much they go mute or stop telling you important information. Oh intelligence!
  - le-mark 3mo ago
    Not at all. Anthropomorphizing them is not productive or desirable imo.
    - CuriouslyC 3mo ago
      Disagree. Some of what we call "anthropomorphizing" is characterizing intelligence, human or otherwise. This reminds me of the people who used to fight against saying animals had personalities, because personalities are a "human thing" and "animals aren't conscious."
      - zahlman 3mo ago

derpified 3mo ago

> Intent understanding Does this mean ChatGPT will stop botsplaining things to me? I get it quite a bit more per unit time from ChatGPT than claude. Maybe that will change now. (By botsplaining I mean when the AI explains some unstated premise of the prompt itself back at me as a correction when in many cases it's the motivation for the question in the first place)

jeffybefffy519 3mo ago
I found changing chatgpt's persona settings helped a lot with this!
embedding-shape 3mo ago
Never had that happen in ChatGPT itself, I almost always use Pro mode whenever I use ChatGPT, but what you say happens a ton in codex, when I look through the session traces it seems to happen because of the automatic compaction, where some assumption the initial pass did gets passed on as a question from the user to the part after compaction, which is a bit confusing. I think it was mentioned somewhere that the compaction got a lot better, but I haven't used GPT-5.6 enough to say if it's actually better or not on that.

exceptione 3mo ago

> Intent understanding: GPT-5.6 can better infer the user’s underlying goal and intended level of work without you specifying every step. Continue to state important constraints, approval boundaries, and success criteria explicitly. I guess this has been achieved by training on user's chat history?

ryukoposting 3mo ago

> Avoid generic brevity instructions y'know, I don't think I will. I really, truly want one-word answers to any binary or multiple-choice question. If I want more, I will ask for it once the model has given its answer.

jitl 3mo ago
that is a specific brevity instruction!
- ryukoposting 3mo ago
  Yeah, but good luck getting any model to obey it.