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O1 isn't a chat model (and that's the point)
- ttul 2y agoFWIW: OpenAI provides advice on how to prompt o1 (https://platform.openai.com/docs/guides/reasoning/advice-on-prompting#advice-on-prompting https://platform.openai.com/docs/guides/reasoning/advice-on-...). Their first bit of advice is to, “Keep prompts simple and direct: The models excel at understanding and responding to brief, clear instructions without the need for extensive guidance.”
- 3abiton 2y agoBut the way they did their PR for O1 made it sound like it was the next step, while in reality it was a side step. A branching from the current direction towards AGI.
- jmcdonald-ut 2y agoThe article links out to OpenAI's advice on prompting, but it also claims: OpenAI does publish advice on prompting o1, but we find it incomplete, and in a sense you can view this article as a “Missing Manual” to lived experience using o1 and o1 pro in practice. To that end, the article does seem to contradict some of the advice OpenAI gives. E.g., the article recommends stuffing the model with as much context as possible... while OpenAI's docs note to include only the most relevant information to prevent the model from overcomplicating its response. I haven't used o1 enough to have my own opinion.
- irthomasthomas 2y agoThose are contradictory. Openai claim that you don't need a manual, since O1 performs best with simple prompts. The author claims it performs better with more complex prompts, but provides no evidence.
- orf 2y agoIn case you missed it OpenAI does publish advice on prompting o1, but we find it incomplete, and in a sense you can view this article as a “Missing Manual” to lived experience using o1 and o1 pro in practice. The last line is important
- irthomasthomas 2y agoBut extraordinary claims require extraordinary proof. Openai tested the model for months and concluded that simple prompts are best. The author claims that complex prompts are best, but cites no evidence.
- orf 2y agoI find it surprising that you think documentation issues are “extraordinary”. You have read literally any documentation before, right?
- irthomasthomas 2y agoI'd just love to see one of the prompt/response pairs demonstrating the technique. Here is an example from my llm-consortium tool. If you want more, you can have them in 5 minutes. https://github.com/irthomasthomas/llm-consortium/blob/main/cfr-example.md https://github.com/irthomasthomas/llm-consortium/blob/main/c...
- ttul 2y agoI mean, OpenAI not only tested the model, they literally trained the model. Training a model involves developing evaluations for the model. It’s a gargantuan effort. I’m fairly certain that OpenAI is the authority on how to prompt o1.
- threatripper 2y agoRequiring only simple prompts surely sells better. I would not assume the documentation provided by OpenAI is totally unbiased and independent of business goals.
- yzydserd 2y agoI think there is a distinction between “instructions”, “guidance” and “knowledge/context”. I tend to provide o1 pro with a LOT of knowledge/context, a simple instruction, and no guidance. I think TFA is advocating same.
- wahnfrieden 2y agoThe advice is wrong
- chikere232 2y agoSo in a sense, being an early adopter for the previous models makes you worse at this one?
- goolulusaurs 2y agoThe reality is that o1 is a step away from general intelligence and back towards narrow ai. It is great for solving the kinds of math, coding and logic puzzles it has been designed for, but for many kinds of tasks, including chat and creative writing, it is actually worse than 4o. It is good at the specific kinds of reasoning tasks that it was built for, much like alpha-go is great at playing go, but that does not actually mean it is more generally intelligent.
- adrianN 2y agoSo-so general intelligence is a lot harder to sell than narrow competence.
- kilroy123 2y agoYes, I don't understand their ridiculous AGI hype. I get it you need to raise a lot of money. We need to crack the code for updating the base model on the fly or daily / weekly. Where is the regular learning by doing? Not over the course of a year, spending untold billions to do it.
- tomohelix 2y agoTechnically, the models can already learn on the fly. Just that the knowledge it can learn is limited to the context length. It cannot, to use the trendy word, "grok" it and internally adjust the weights in its neural network yet. To change this you would either need to let the model retrain itself every time it receives new information, or to have such a great context length that there is no effective difference. I suspect even meat models like our brains is still struggling to do this effectively and need a long rest cycle (i.e. sleep) to handle it. So the problem is inherently more difficult to solve than just "thinking". We may even need an entire new architecture different from the neural network to achieve this.
- KuriousCat 2y agoOnly small problem is that models are neither thinking nor understanding, I am not sure how this kind of wording is allowed with these models.
- fpgaminer 2y agoIt does seem like individual prompting styles greatly effects the performance of these models. Which makes sense of course, but the disparity is a lot larger than I would have expected. As an example, I'd say I see far more people in the HN comments preferring Claude over everything else. This is in stark contrast to my experience, where ChatGPT has and continues to be my go to for everything. And that's on a range of problems: general questions, coding tasks, visual understanding, and creative writing. I use these AIs all day, every day as part of my research, so my experience is quite extensive. Yet in all cases Claude has performed significantly worse for me. Perhaps it just comes down to the way that I prompt versus the average HN user? Very odd. But yeah, o1 has been a _huge_ leap in my experience. One huge thing, which OpenAI's announcement mentions as well, is that o1 is more _consistently_ strong. 4o is a great model, but sometimes you have to spin the wheel a few times. I much more rarely need to spin o1's wheel, which mostly makes up for its thinking time. (Which is much less these days compared to o1-preview). It also has much stronger knowledge. So far it has solved a number of troubleshooting tasks that there were _no_ fixes for online. One of them was an obscure bug in libjpeg. It's also better at just general questions, like wanting to know the best/most reputable store for something. 4o is too "everything is good! everything is happy!" to give helpful advice here. It'll say Temu is a "great store for affordable options." That kind of stuff. Whereas o1 will be more honest and thus helpful. o1 is also significantly better at following instructions overall, and inferring meaning behind instructions. 4o will be very literal about examples that you give it whereas o1 can more often extrapolate. One surprising thing that o1 does that 4o has never done, is that it _pushes back_. It tells me when I'm wrong (and is often right!). Again, part of that being less happy and compliant. I have had scenarios where it's wrong and it's harder to convince it otherwise, so it's a double edged sword, but overall it has been an improvement in the bot's usefulness. I also find it interesting that o1 is less censored. It refuses far less than 4o, even without coaxing, despite its supposed ability to "reason" about its guidelines :P What's funny is that the "inner thoughts" that it shows says that it's refusing, but its response doesn't. Is it worth $200? I don't think it is, in general. It's not really an "engineer" replacement yet, in that if you don't have the knowledge to ask o1 the right questions it won't really be helpful. So you have to be an engineer for it to work at the level of one. Maybe $50/mo? I haven't found o1-pro to be useful for anything; it's never really given better responses than o1 for me. (As an aside, Gemini 2.0 Flash Experimental is _very_ good. It's been trading blows with even o1 for some tasks. It's a bit chaotic, since its training isn't done, but I rank it at about #2 between all SOTA models. A 2.0 Pro model would likely be tied with o1 if Google's trajectory here continues.)
- refulgentis 2y agoThis is a bug, and a regression, not a feature. It's odd to see it recast as "you need to give better instructions [because it's different]" -- you could drop the "because it's different" part, and it'd apply to failure modes in all models. It also begs the question of how it's different: and that's where the rationale gets cyclical. You have to prompt it different because it's different because you have to prompt it different. And where that really gets into trouble is the "and that's the point" part -- as the other comment notes, it's expressly against OpenAI's documentation and thus intent. I'm a yuge AI fan. Models like this are a clear step forward. But it does a disservice to readers to leave the impression that the same techniques don't apply to other models, and recasts a significant issue as design intent.
- torginus 2y agoI wouldn't be so harsh - you cold have a 4o style LLM turn vague user queries into precise constraints for an o1 style AI - this is how a lot of stable diffusion image generators work already.
- refulgentis 2y agoCorrect, you get it: its turtles all the way down, not "it's different intentionally"
- inciampati 2y agoLooking at o1's behavior, it seems there's a key architectural limitation: while it can see chat history, it doesn't seem able to access its own reasoning steps after outputting them. This is particularly significant because it breaks the computational expressivity that made chain-of-thought prompting work in the first place—the ability to build up complex reasoning through iterative steps. This will only improve when o1's context windows grow large enough to maintain all its intermediate thinking steps, we're talking orders of magnitude beyond current limits. Until then, this isn't just a UX quirk, it's a fundamental constraint on the model's ability to develop thoughts over time.
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- adamgordonbell 2y agoI'd love to see some examples, of good and bad prompting of o1 I'll admit I'm probably not using O1 well, but I'd learn best from examples.
- swyx 2y agocoauthor/editor here! we recorded a followup conversation after the surprise popularity of this article breaking down some more thoughts and behind the scenes: https://youtu.be/NkHcSpOOC60?si=3KvtpyMYpdIafK3U https://youtu.be/NkHcSpOOC60?si=3KvtpyMYpdIafK3U
- cebert 2y agoThanks for sharing this video, swyx. I learned a lot from listening to it. I hadn’t considered checking prompts for a project into source control. This video has also changed my approach to prompting in the future.
- swyx 2y agothanks for watching! “prompts in source control” is kinda like “configs in source control” for me. recommended for small projects, but at scale eventually you wanna abstract it out into some kind of prompt manager software for others to use and even for yourself to track and manage over time. git isnt the right database for everything.
- dutchbookmaker 2y agoGreat stuff, thanks for this.
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- geor9e 2y agoInstead of learning the latest workarounds for the kinks and quirks of a beta AI product, I'm going to wait 3 weeks for the advice to become completely obsolete
- thornewolf 2y agoTo be fair, the article basically says "ask the LLM for what you want in detail"
- fullstackwife 2y agogreat advice, but difficult to apply given very small context window of o1 models
- jameslk 2y agoThe churn is real. I wonder if so much churn due to innovation in a space can prevent enough adoption such that it actually reduces innovation
- miltonlost 2y agoA constantly changing "API" coupled with a inherently unreliable output is not conducive to stable business.
- b112 2y agoUnless your business is customer service reps, with no ability to do anything but read scripts, who have no real knowledge of how things actually work. Then current AI is basically the same, for cheap.
- dartos 2y agoMany service reps do have some expertise in the systems they support. Once you get past the tier 1 incoming calls, support is pretty specialized.
- samrolken 2y agoI have a lot of luck using 4o to build and iterate on context and then carry that into o1. I’ll ask 4o to break down concepts, make outlines, identify missing information and think of more angles and options. Then at the end, switch on o1 which can use all that context.
- sklargh 2y agoThis echoes my experience. I often use ChatGPT to help with D&D module design and I found that O1 did best when I told it exactly what k required, dumped in a large amount of info and did not expect to use it to iterate multiple times.
- keizo 2y agoI made a tool for manually collecting context. I use it when copying and pasting multiple files is cumbersome: https://pypi.org/project/ggrab/ https://pypi.org/project/ggrab/
- franze 2y agoi creates thisismy.franzai.com for the same reason
- miltonlost 2y agooh god using an LLM for medical advice? and maybe getting 3/5 right? Barely above a coin flip. And that Warning section? "Do not be wrong. Give the correct names." That this is necessary to include is an idiotic product "choice" since its non-inclusion implies the bot is able to be wrong and give wrong names. This is not engineering.
- isoprophlex 2y agoNot if you're selecting out of 10s or 100s of possible diagnoses
- miltonlost 2y ago?????? What? > Just for fun, I started asking o1 in parallel. It’s usually shockingly close to the right answer — maybe 3/5 times. More useful for medical professionals — it almost always provides an extremely accurate differential diagnosis. THIS IS DANGEROUS TO TELL PEOPLE TO DO. OpenAI is not a medical professional. Stop using chatbots for medical diagnoses. 60% is not almost always extremely accurate. This whole post, because of this bullet point, shows the author doesn't actually know the limitations of the product they're using and instead passing along misinformation. Go to a doctor, not your chatbot.
- MarcoZavala 2y ago[dead]
- simonw 2y agoI honestly think trusting exclusively your own doctor is a dangerous thing to do as well. Doctors are not infallible. It's worth putting in some extra effort yourself, which may include consulting with LLMs provided you don't trust those blindly and are sensible about how you incorporate hints they give you into your own research. Nobody is as invested in your own health as you are.
- PollardsRho 2y agoIt's hard to characterize the entropy of the distribution of potential diseases given a presentation: even if there are in theory many potential diagnoses, in practice a few will be a lot more common. It doesn't really matter how much better the model is than random chance on a sample size of 5, though. There's a reason medicine is so heavily licensed: people die when they get uninformed advice. Asking o1 if you have skin cancer is gambling with your life. That's not to say AI can't be useful in medicine: everyone doesn't have a dermatologist friend, after all, and I'm sure for many underserved people basic advice is better than nothing. Tools could make the current medical system more efficient. But you would need to do so much more work than whatever this post did to ascertain whether that would do more good than harm. Can o1 properly direct people to a medical expert if there's a potentially urgent problem that can't be ruled out? Can it effectively disclaim its own advice when asked about something it doesn't know about, the way human doctors refer to specialists?
- isoprophlex 2y agoPeople agreeing and disagreeing about the central thesis of the article, which is fine because i enjoy the discussion... no matter where you stand in the specific o1/o3 discussion the concept of "question entropy" is very enlightening. what is the question of theoretical minimum complexity that still solves your question adequately? or for a specific model, are its users capable of supplying the minimum required intellectual complexity the model needs? Would be interesting to quantify these two and see if our models are close to converging on certain task domains.
- dutchbookmaker 2y agoGood stuff. I am going to try to start measuring my prompts. Thinking about it, I am not sure what the entropy is for the above vs "start measuring prompts".
- isoprophlex 2y agoThat's a tough one, I'm not sure how to get a quantifyable number on it except for painstakingly ablating a prompt until the answer you get becomes significantly degraded. But then still, how do you measure how much you have ablated your prompt? How do you measure objectively how badly the answer has degraded?
- iovrthoughtthis 2y agothis is hilarious
- irthomasthomas 2y agoCan you provide prompt/response pairs? I'd like to test how other models perform using the same technique.
- swalsh 2y agoWork with chat bots like a junior dev, work with o1 like a senior dev.
- timewizard 2y ago> To justify the $200/mo price tag, it just has to provide 1-2 Engineer hours a month > Give a ton of context. Whatever you think I mean by a “ton” — 10x that. One step forward. Two steps back.
- martythemaniak 2y agoOne thing I'd like to experiment with is "prompt to service". I want to take an existing microservice of about 3-5kloc and see if I can write a prompt to get o1 to generate the entire service, proper structure, all files, all tests, compiles and passes etc. o1 certainly has the context window to do this at 200k input and 100k output - code is ~10 tokens per line of code, so you'd need like 100k input and 50k output tokens. My approach would be: - take an exemplar service, dump it in the context - provide examples explaining specific things in the exemplar service - write a detailed formal spec - ask for the output in JSON to simplify writing the code - [{"filename":"./src/index.php", "contents":"<?php...."}] The first try would inevitably fail, so I'd provide errors and feedback, and ask for new code (ie complete service, not diffs or explanations), plus have o1 update and rewrite the spec based on my feedback and errors. Curious if anyone's tried something like this.
- deleted 2y ago[deleted]
- patrickhogan1 2y agoThe buggy nature of o1 in ChatGPT is what prevents me from using it the most. Waiting is one thing, but waiting to return to a prompt that never completes is frustrating. It’s the same frustration you get from a long running ‘make/npm/brew/pip’ command that errors out right as it’s about to finish. One pattern that’s been effective is 1. Use Claude Developer Prompt Generator to create a prompt for what I want. 2. Run the prompt on o1 pro mode
- inciampati 2y agoo1 appears to not be able to see it's own reasoning traces. Or it's own context is potentially being summarized to deal with the cost of giving access to all those chain of thought traces and the chat history. This breaks the computational expressivity or chain of thought, which supports universal (general) reasoning if you have reliable access to the things you've thought, and is threshold circuit (TC0) or bounded parallel pattern matcher when not.
- PoignardAzur 2y agoMy understanding is that o1's chain-of-thought tokens are in its own internal embedding, and anything human-readable the UI shows you is a translation of these CoT tokens into natural language.
- inciampati 2y agoWhere is that documented? Fwiw, interactive use suggests they are not available to later invocations of the model. Any evidence this isn't the case?
- inciampati 2y agoI found this documentation from openai that supports my hunch: https://platform.openai.com/docs/guides/reasoning/advice-on-prompting https://platform.openai.com/docs/guides/reasoning/advice-on-... The reasoning tokens from each step are lost. And there is no indication that they are different tokens than regular tokens.
- mediumsmart 2y agoI agree with the article and found the non pro version very good at creating my local automation tool chain. It writes the scripts for every step and then you hand them all back to it and it links them up as a single dothiscomplicatedthing.sh