Show notes
Riley Goodside is a staff prompting engineer at Scale AI. Previously working in data science, he is often seen as the default for the new role of a “prompt engineer.” He regularly posts incisive prompts that illicit notable behavior from the most popular AI models.I really resonated with this saying from Anthropic’s recent podcast on prompt engineering — “now we write essays and treat them as code.” In order to be good at prompting, you need to understand that natural language operates as our code used to.This episode is a masterclass on why you should care about prompting and how it impacts results. Of course, there’s a bunch of great discussion on recent models that reflect the need for different and or better prompting. Enjoy it!Listen on Apple Podcasts, Spotify, and where ever you get your podcasts. For other Interconnects interviews, go here.We mention:* Prompting to push the frontier of AI models,* Post-training and prompting interaction,* Prompting base models,* o1, Reflection 70B, reasoning,* Scale’s leaderboard, evaluation tricks, evaluation needs,* PlanSearch paper* Julius AI* “The hottest programming language is english”* “Think silently” instructions* Scale Leaderboard and Humanity’s Last Exam* ChatML formattingChapters* [00:00:09] Introduction* [00:02:40] Riley's path to LLMs* [00:07:54] Impact of ChatGPT on prompt engineering* [00:12:03] OpenAI's o1* [00:18:21] Autoregressive inference and prompting sensitivities* [00:24:48] Reflection 70B model and its implications* [00:28:00] Impact of prompting on evaluation* [00:32:43] Prompting vs. Google search* [00:46:55] Prompting and RLHF/post-training* [00:56:57] Prompting of AI agents* [01:01:20] Importance of hands-on experience with language models* [01:05:00] Importance and challenges of AI model evaluationTranscriptBuilt with smol-podcaster.Nathan L. [00:01:08]: Hey, Riley, welcome to the show.Riley G. Hey, Nathan, great to be here.Nathan L. [00:01:14]: Yeah, so for the audience here, I mostly wanted to try to, as I work on post-training a lot and I see my own difficulty in taking prompting seriously and the things that I don't think that we are doing enough, and I don't see any reason why it can't be scientific in how we do prompting. So that's my biggest goal with this. I think there's a lot of podcasts where we could kind of say, like, what is the history of prompting? Where is it going? And that's easy to kind of redo. And I still find it interesting, but I just don't think there's enough people talking about the role of prompting in evaluation, how prompting changes with how your post-training models, because we're trying to take that seriously and how we have a post-training setup, but we just like regularly run into these things like system prompts aren't handled well, how to release a model of a system prompt. So that's the tone that I'm trying to get to when I ask these questions. And also OpenAI's 01 model just came out, so I'm definitely going to get onto that pretty quickly because that's what everyone's excited about. I like to start with background just to kind of get to know people, because a lot of this is just, I want to talk to interesting people in AI, is like, how did you become interested in prompting? I think I've seen your background in data science and then your joint scale around when Chad2BT came out, which is fun timing, but like, how did you become maybe obsessed with this, but like the focal point of your work?Riley G. [00:02:40]: Yeah, I have sort of an unusual introduction to large language models. For most of my career, I've been a data scientist, mostly in the on-mandating industry. I was at OkCupid and Grindr. And after I left Grindr, I took sort of a sabbatical to educate myself, I guess, about the progress in large language models. It was around the time that GPT-3 codecs had just come out. And that was where I think I started to become really interested because I was following along with maybe, certainly when GPT-2 came out, the examples there wowed me as much as they wowed the rest of the world, I think, with the example of the news article about the unicorn and all that. And not long after that, we had AI Dungeon, and I played around with AI Dungeon a bit. But at that point, language models seemed to be mostly about language, that they were sort of very heavily focused on stylistic mimicry and creative writing and so on. And when Codex came out, it really started this thought of that text is a more universal interface than we were giving you credit for, that language models might be more broadly useful. And I just became very excited in a practical sense of what these models could do for what I kind of intuited was very boilerplate-like data science code, that I thought of like most of the Python and Julia and R and things that I've written over my career, this seemed like stuff that an LLM could handle. And that was sort of one of its early strong points. So I was playing around with, I think one of my first projects was a VS Code extension that had some kind of integration with Codex. But I never really shipped anything out of it. And mostly what it transitioned into pretty quickly was playing around with posting prompting examples on Twitter, because when I looked out online to find what were people saying about how to prompt these models, there really wasn't much out there. And so I had to kind of resort to just like the few examples that had been circulating in viral screenshots of humorous completions and so on, of like the results that people got out of it. And I started posting those examples. I started following academics and low-level engineers at the research labs and anyone that was working in shipping language models I thought were interesting. And elbowed my way in.Nathan L. [00:05:18]: I have more questions on this, because I find it like, some people find, there's this whole like Twitter dynamic of like, you find so much signal there, but the question is like, how much does it generalize? Because there's so many of the lessons you can learn from these models, from these examples. I think the straw, like the number of R's in strawberry things is the current one. And then, and it's like, do you get a sense that these are transient or are these kind of repeated themes? And like, how should you read these examples to try to extract themes from them? If like, I've followed you for a while, and a lot of people do, and you're more insightful in how you post them. If you post these threads with like multiple tries and stuff like this, like, should people be doing that when they see something pop up?Riley G. [00:06:03]: I think so. I also would say that Twitter is a very different river to step into now than it was back then. At the point that I started doing this, like, nobody was really talking about these things that much, or to the extent they were, it was sort of fleeting. It was like, wow, look at this, and then they on to the next thing. And I think the thing that's very different now is just that because there are so many new entrants in AI and LLM, there's a lot of rehashing of the basics. And I think a lot of people in the industry would tell you that the popular examples that you see around of like, how many R's are in strawberry, or some of the ones that I'm partially responsible for, popularizing at least. I think like, these things are really just like, rookie mistakes in some sense, right? That these are things that we've long known language models can't do. And it just keeps popping up as a surprising quirk of language models that I think the public is just confused that something could be so good at so many other things and so bad at this. Right? That is seemingly trivial task, and that is hard to explain to people. And the answer to that hasn't really changed much in the past few years. They're generally bad at spelling for kind of the same reasons they were bad at spelling two or three years ago.Nathan L. [00:07:27]: Yeah. I mean, like, how did these things change with ChatGPT? Because ChatGPT is like the introduction of RLHF into these models. And I think, I didn't write this down as a question, but there's like the difference in patronizing base models and instruction models and RLHF models, which I think that for most of this discussion, it's like the end model, the like chat RLHF model is the one that people think about. But was that a big transition point in your work or is it just kind of plugging along? Right.Riley G. [00:07:54]: I mean, I would say, I don't think it's any understatement to say that, or sorry, any overstatement to say that, that the release of ChatGPT was probably the single biggest event in the history of prompt engineering in that prompt engineering became drastically easier after ChatGPT came out. And most other models learned from the ChatGPT way of doing things, right? That they, like, I think people forget just how fiddly prompt engineering used to be, right? Like people today don't think about things like frequency and presence penalties, right? They used to be that by default, you would get very repetitious output and you had to work to avoid that. People forgot about like, don't end your prompt in a space, right? That you had to understand how tokenization worked at all times, because like, if you put an extra space in there, you were going to go out of distribution. I think that, or another one that I think is particularly vivid for me is Yobi Reel that in June of 2022, Douglas Hofstadter had a piece in The Economist showing the, what he called the hollowness of GPT-3's understanding of the world, that it failed on various simple questions. Like, when was the Golden Gate Bridge transported for the second time across Egypt and so on? And someone, I believe it was Nick Camerota of OpenAI, showed that you could fix almost all of these just by telling the model that if you gave it a silly question, say Yobi Reel instead of answering it, right? That models had to be prompted with the possibility that they were allowed to say, I don't know, or, you know, that's a dumb question, right? You know, like there is no answer, right?Nathan L. [00:09:34]: This is like, we've added the anthropic system prompt to our AI2 models, and we're like, this doesn't change the evals at all, but it makes the behavior something that we like more. Because I think culturally we're somewhat similar to anthropic, it's like we want to express uncertainty, we want the model to say that, I don't know, and a lot of that is in the system prompt of anthropic models.Riley G. [00:09:51]: Right. And I think that really, you know, it's another microcosm of just how messy all this is, that what people like is a very different thing from how good are the models. I think, you know, LMSYS had a great blog post recently talking about like stylistic bias and output, that models will be rated as better if they do things like put their output into the format of a bulleted list with bold initial words on each label point. So there's like cheap tricks like that, that will make people like your output better or make them perceive it as, you know, more authoritative or, you know, more comprehensive that you kind of have to control for and just going by preference. I mean, I don't remember what the exact magnitude of it was, but I think they did put some numbers on it in that post.Nathan L. [00:10:42]: Like, do you think you could handle all of that? Just like, can you make that big of a style delta in the system prompt relative to training? Is kind of what I'm wondering. Like if we release a model at AI2 and it's decent, but then we put a detailed system prompt that it's like, whatever possible, you should put your models into a list format with bolded headings and use markdown. Like, do you think we would get a 50 point bump on ElmSys?Riley G. [00:11:06]: Maybe not on ElmSys in particular, being as they're trying to correct for this actively. But presumably it would have worked at one point, right? So I think that's, you know, that says something that these, or another great example, I think that's really clear of like why human preference isn't, you know, always the answer. I saw somebody on Twitter once that was really impressed by some anonymous model on ElmSys that was able to produce an ASCII art drawing of a unicorn. And it was a great drawing. And, but when I searched for like specific details of that drawing, I found that it was just in some like widely circulated list of ASCII art drawings. And it was a verbatim regurgitation of some signed work that somebody had made. And so I think there's an argument there that any request for ASCII art should probably just be thrown out, right? That a human's preference of how good an Elm is at ASCII art maybe just does not matter because like, it's so likely to be regurgitated or at least like figurative things, maybe diagrams are okay and so on. Yeah. Yeah. Okay.Nathan L. [00:12:03]: We've touched on multiple of the things I want to get to in the future, but you kind of said that Chad2PT was the biggest moment for prompt engineering. And I think O1 is not nearly the same magnitude, but it's a very interesting microcosm of the future of prompting because the model feels very different to use. OpenAI has explicitly told us we need to prompt it differently. But I think my guess is that in the long-term, they're going to figure out how to train this model so that the behavior is not indistinguishable from their GPT models, but that it's not as sensitive to prompting and whatever you throw at it, it's going to work. Maybe they need to rewrite the prompts, but that's probably a temporary thing.Nathan L. [00:12:45]: Two questions to me is simpler. What do you think when you see them giving you like, oh, we need to have these new prompting instructions to use it differently? Do you agree with my long-term convergence idea?Riley G. [00:12:57]: I definitely agree. I think that there's an argument for seeing prompt engineering as kind of the experimental next branch of language models, right? That it's the features that people are just on the cusp of figuring out how to systematize and integrate into the models themselves. And to the extent that somebody comes up with a prompt engineering idea that is just so good of an idea that it's worth applying to literally every prompt, then it will be integrated into the models and you'll stop calling it a model, you'll call it a system and it'll have some auxiliary second model. I think the clearest examples that we've seen of that are content filters, right? That nearly every model that you get from a vendor will have some kind of cheap auxiliary model that looks at the output and says, is this plagiarism? Is this, or not plagiarism, but regurgitation of copyrighted work, right? Are you reciting Harry Potter word for word? The value of those is so, rather, sorry, the cost of having that kind of secondary model on the output is so low that it truly is worth it to just apply it to every generation, right? And we haven't seen too many examples of that on the input side, but they're starting to appear, I think. I think we've seen from anthropic evidence that they make modifications to user inputs based on certain conditions that they detect if you're asking about some particular feature, they modify the prompt if you are. And I think that's a common pattern in a lot of applications.Nathan L. [00:14:31]: I'm guessing they've seen some public people kind of using the model. I haven't heard…
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