8 ms·

> Notice the language: “deeply”, “in great details”, “intricacies”, “go through everything”. This isn’t fluff. Without these words, Claude will skim. It’ll read

• fragmede 8mo ago
  Yeah, it's definitely a strange new world we're in, where I have to "trick" the computer into cooperating. The other day I told Claude "Yes you can", and it went off and did something it just said it couldn't do!
  • itypecode 8mo ago
    Solid dad move. XD
    • wilkystyle 8mo ago
      Is parenting making us better at prompt engineering, or is it the other way around?
      • fragmede 8mo ago
        Better yet, I have Codex, Gemini, and Claude as my kids, running around in my code playground. How do I be a good parent and not play favorites?
        • itypecode 8mo ago
          We all know Gemini is your artsy, Claude is your smartypants, and Codex is your nerd.
  • bpodgursky 8mo ago
    You bumped the token predictor into the latent space where it knew what it was doing : )
  • optimalsolver 8mo ago
    The little language model that could.
• deleted 8mo ago
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• jcdavis 8mo ago
  Its a wild time to be in software development. Nobody(1) actually knows what causes LLMs to do certain things, we just pray the prompt moves the probabilities the right way enough such that it mostly does what we want. This used to be a field that prided itself on deterministic behavior and reproducibility. Now? We have AGENTS.md files that look like a parent talking to a child with all the bold all-caps, double emphasis, just praying that's enough to be sure they run the commands you want them to be running (1 Outside of some core ML developers at the big model companies)
  • chickensong 8mo ago
    For Claude at least, the more recent guidance from Anthropic is to not yell at it. Just clear, calm, and concise instructions.
    • trueno 8mo ago
      wait seriously? lmfao thats hilarious. i definitely treat claude like shit and ive noticed the falloff in results. if there's a source for that i'd love to read about it.
      • defrost 8mo ago
        Consciousness is off the table but they absolutely respond to environmental stimulus and vibes. See, uhhh, https://pmc.ncbi.nlm.nih.gov/articles/PMC8052213/ https://pmc.ncbi.nlm.nih.gov/articles/PMC8052213/ and maybe have a shot at running claude while playing Enya albums on loop. /s (??)
        • trueno 8mo ago
          i have like the faintest vague thread of "maybe this actually checks out" in a way that has shit all to do with consciousness sometimes internet arguments get messy, people die on their hills and double / triple down on internet message boards. since historic internet data composes a bit of what goes into an llm, would it make sense that bad-juju prompting sends it to some dark corners of its training model if implementations don't properly sanitize certain negative words/phrases ? in some ways llm stuff is a very odd mirror that haphazardly regurgitates things resulting from the many shades of gray we find in human qualities.... but presents results as matter of fact. the amount of internet posts with possible code solutions and more where people egotistically die on their respective hills that have made it into these models is probably off the charts, even if the original content was a far cry from a sensible solution. all in all llm's really do introduce quite a bit of a black box. lot of benefits, but a ton of unknowns and one must be hyperviligant to the possible pitfalls of these things... but more importantly be self aware enough to understand the possible pitfalls that these things introduce to the person using them. they really possibly dangerously capitalize on everyones innate need to want to be a valued contributor. it's really common now to see so many people biting off more than they can chew, often times lacking the foundations that would've normally had a competent engineer pumping the brakes. i have a lot of respect/appreciation for people who might be doing a bit of claude here and there but are flat out forward about it in their readme and very plainly state to not have any high expectations because _they_ are aware of the risks involved here. i also want to commend everyone who writes their own damn readme.md. these things are for better or for worse great at causing people to barrel forward through 'problem solving', which is presenting quite a bit of gray area on whether or not the problem is actually solved / how can you be sure / do you understand how the fix/solution/implementation works (in many cases, no). this is why exceptional software engineers can use this technology insanely proficiently as a supplementary worker of sorts but others find themselves in a design/architect seat for the first time and call tons of terrible shots throughout the course of what it is they are building. i'd at least like to call out that people who feel like they "can do everything on their own and don't need to rely on anyone" anymore seem to have lost the plot entirely. there are facets of that statement that might be true, but less collaboration especially in organizations is quite frankly the first steps some people take towards becoming delusional. and that is always a really sad state of affairs to watch unfold. doing stuff in a vaccuum is fun on your own time, but forcing others to just accept things you built in a vaccuum when you're in any sort of team structure is insanely immature and honestly very destructive/risky. i would like to think absolutely no one here is surprised that some sub-orgs at Microsoft force people to use copilot or be fired, very dangerous path they tread there as they bodyslam into place solutions that are not well understood. suddenly all the leadership decisions at many companies that have made to once again bring back a before-times era of offshoring work makes sense: they think with these technologies existing the subordinate culture of overseas workers combined with these techs will deliver solutions no one can push back on. great savings and also no one will say no.
• wilkystyle 8mo ago
  The author is referring to how the framing of your prompt informs the attention mechanism. You are essentially hinting to the attention mechanism that the function's implementation details have important context as well.
• MattGaiser 8mo ago
  One of the well defined failure modes for AI agents/models is "laziness." Yes, models can be "lazy" and that is an actual term used when reviewing them. I am not sure if we know why really, but they are that way and you need to explicitly prompt around it.
  • kannanvijayan 8mo ago
    I've encountered this failure mode, and the opposite of it: thinking too much. A behaviour I've come to see as some sort of pseudo-neuroticism. Lazy thinking makes LLMs do surface analysis and then produce things that are wrong. Neurotic thinking will see them over-analyze, and then repeatedly second-guess themselves, repeatedly re-derive conclusions. Something very similar to an anxiety loop in humans, where problems without solutions are obsessed about in circles.
    • denimnerd42 8mo ago
      yeah i experienced this the other day when asking claude code to build an http proxy using an afsk modem software to communicate over the computers sound card. it had an absolute fit tuning the system and would loop for hours trying and doubling back. eventually after some change in prompt direction to think more deeply and test more comprehensively it figured it out. i certainly had no idea how to build a afsk modem.
• ChadNauseam 8mo ago
  The disconnect might be that there is a separation between "generating the final answer for the user" and "researching/thinking to get information needed for that answer". Saying "deeply" prompts it to read more of the file (as in, actually use the `read` tool to grab more parts of the file into context), and generate more "thinking" tokens (as in, tokens that are not shown to the user but that the model writes to refine its thoughts and improve the quality of its answer).
• hashmap 8mo ago
  these sort-of-lies might help: think of the latent space inside the model like a topological map, and when you give it a prompt, you're dropping a ball at a certain point above the ground, and gravity pulls it along the surface until it settles. caveat though, thats nice per-token, but the signal gets messed up by picking a token from a distribution, so each token you're regenerating and re-distorting the signal. leaning on language that places that ball deep in a region that you want to be makes it less likely that those distortions will kick it out of the basin or valley you may want to end up in. if the response you get is 1000 tokens long, the initial trajectory needed to survive 1000 probabilistic filters to get there. or maybe none of that is right lol but thinking that it is has worked for me, which has been good enough
  • noduerme 8mo ago
    Hah! Reading this, my mind inverted it a bit, and I realized ... it's like the claw machine theory of gradient descent. Do you drop the claw into the deepest part of the pile, or where there's the thinnest layer, the best chance of grabbing something specific? Everyone in everu bar has a theory about claw machines. But the really funny thing that unites LLMs with claw machines is that the biggest question is always whether they dropped the ball on purpose. The claw machine is also a sort-of-lie, of course. Its main appeal is that it offers the illusion of control. As a former designer and coder of online slot machines... totally spin off into pages on this analogy, about how that illusion gets you to keep pulling the lever... but the geographic rendition you gave is sort of priceless when you start making the comparison.
  • basch 8mo ago
    My mental model for them is plinko boards. Your prompt changes the spacing between the nails to increase the probability in certain directions as your chip falls down.
    • hashmap 8mo ago
      i literally suggested this metaphor earlier yesterday to someone trying to get agents to do stuff they wanted, that they had to set up their guardrails in a way that you can let the agents do what they're good at, and you'll get better results because you're not sitting there looking at them. i think probably once you start seeing that the behavior falls right out of the geometry, you just start looking at stuff like that. still funny though.
• stingraycharles 8mo ago
  It’s actually really common. If you look at Claude Code’s own system prompts written by Anthropic, they’re littered with “CRITICAL (RULE 0):” type of statements, and other similar prompting styles.
  • Scrapemist 8mo ago
    Where can I find those?
    • stingraycharles 8mo ago
      This analysis is a good starting point: https://southbridge-research.notion.site/Prompt-Engineering-The-Art-of-Instructing-AI-2055fec70db181369002dcdea7d9e732 https://southbridge-research.notion.site/Prompt-Engineering-...
• Betelbuddy 8mo ago
  Its very logical and pretty obvious when you do code generation. If you ask the same model, to generate code by starting with: - You are a Python Developer... or - You are a Professional Python Developer... or - You are one of the World most renowned Python Experts, with several books written on the subject, and 15 years of experience in creating highly reliable production quality code... You will notice a clear improvement in the quality of the generated artifacts.
  • obiefernandez 8mo ago
    My colleague swears by his DHH claude skill https://danieltenner.com/dhh-is-immortal-and-costs-200-m/ https://danieltenner.com/dhh-is-immortal-and-costs-200-m/
    • bavell 8mo ago
      Haha, this reminds me of all the stable diffusion "in the style of X artist" incantations.
  • haolez 8mo ago
    That's different. You are pulling the model, semantically, closer to the problem domain you want it to attack. That's very different from "think deeper". I'm just curious about this case in specific :)
    • argee 8mo ago
      I don't know about some of those "incantations", but it's pretty clear that an LLM can respond to "generate twenty sentences" vs. "generate one word". That means you can indeed coax it into more verbosity ("in great detail"), and that can help align the output by having more relevant context (inserting irrelevant context or something entirely improbable into LLM output and forcing it to continue from there makes it clear how detrimental that can be). Of course, that doesn't mean it'll definitely be better, but if you're making an LLM chain it seems prudent to preserve whatever info you can at each step.
  • gehsty 8mo ago
• popalchemist 8mo ago
  Strings of tokens are vectors. Vectors are directions. When you use a phrase like that you are orienting the vector of the overall prompt toward the direction of depth, in its map of conceptual space.
• nostrademons 8mo ago
  It's the attention mechanism at work, along with a fair bit of Internet one-up-manship. The LLM has ingested all of the text on the Internet, as well as Github code repositories, pull requests, StackOverflow posts, code reviews, mailing lists, etc. In a number of those content sources, there will be people saying "Actually, if you go into the details of..." or "If you look at the intricacies of the problem" or "If you understood the problem deeply" followed by a very deep, expert-level explication of exactly what you should've done differently. You want the model to use the code in the correction, not the one in the original StackOverflow question. Same reason that "Pretend you are an MIT professor" or "You are a leading Python expert" or similar works in prompts. It tells the model to pay attention to the part of the corpus that has those terms, weighting them more highly than all the other programming samples that it's run across.
  • deleted 8mo ago
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  • r0b05 8mo ago
    This is such a good explanation. Thanks
  • xscott 8mo ago
    Of course I can't be certain, but I think the "mixture of experts" design plays into it too. Metaphorically, there's a mid-level manager who looks at your prompt and tries to decide which experts it should be sent to. If he thinks you won't notice, he saves money by sending it to the undergraduate intern. Just a theory.
    • victorbjorklund 8mo ago
      Notice that MOE isn’t different experts for different types of problems. It’s per token and not really connect to problem type. So if you send a python code then the first one in function can be one expert, second another expert and so on.
      • dotancohen 8mo ago
• ambicapter 8mo ago
  Maybe the training data that included the words like "skim" also provided shallower analysis than training that was close to the words "in great detail", so the LLM is just reproducing those respective words distribution when prompted with directions to do either.
• scuff3d 8mo ago
  How anybody can read stuff like this and still take all this seriously is beyond me. This is becoming the engineering equivalent of astrology.
  • fragmede 8mo ago
    Feel free to run your own tests and see if the magic phrases do or do not influence the output. Have it make a Todo webapp with and without those phrases and see what happens!
    • scuff3d 8mo ago
      That's not how it works. It's not on everyone else to prove claims false, it's on you (or the people who argue any of this had a measurable impact) to prove it actually works. I've seen a bunch of articles like this, and more comments. Nobody I've ever seen has produced any kind of measurable metrics of quality based on one approach vs another. It's all just vibes. Without something quantifiable it's not much better then someone who always wears the same jersey when their favorite team plays, and swears they play better because of it.
      • tokioyoyo 8mo ago
        Do you actively use LLMs to do semi-complex coding work? Because if not, it will sound mumbo-jumbo to you. Everyone else can nod along and read on, as they’ve experienced all of it first hand.
        • scuff3d 8mo ago
          You've missed the point. This isn't engineering, it's gambling. You could take the exact same documents, prompts, and whatever other bullshit, run it on the exact same agent backed by the exact same model, and get different results every single time. Just like you can roll dice the exact same way on the exact same table and you'll get two totally different results. People are doing their best to constrain that behavior by layering stuff on top, but the foundational tech is flawed (or at least ill suited for this use case). That's not to say that AI isn't helpful. It certainly is. But when you are basically begging your tools to please do what you want with magic incantations, we've lost the fucking plot somewhere.
• giancarlostoro 8mo ago
  The LLM will do what you ask it to unless you don't get nuanced about it. Myself and others have noticed that LLM's work better when your codebase is not full of code smells like massive godclass files, if your codebase is discrete and broken up in a way that makes sense, and fits in your head, it will fit in the models head.
• winwang 8mo ago
  Apparently LLM quality is sensitive to emotional stimuli? "Large Language Models Understand and Can be Enhanced by Emotional Stimuli": https://arxiv.org/abs/2307.11760 https://arxiv.org/abs/2307.11760
• nazgul17 8mo ago
  It's very much believable, to me. In image generation, it's fairly common to add "masterpiece", for example. I don't think of the LLM as a smart assistant that knows what I want. When I tell it to write some code, how does it know I want it to write the code like a world renowned expert would, rather than a junior dev? I mean, certainly Anthropic has tried hard to make the former the case, but the Titanic inertia from internet scale data bias is hard to overcome. You can help the model with these hints. Anyway, luckily this is something you can empirically verify. This way, you don't have to take anyone's word. If anything, if you find I'm wrong in your experiments, please share it!
  • pixelmelt 8mo ago
    Its effectiveness is even more apparent with older smaller LLMs, people who interact with LLMs now never tried to wrangle llama2-13b into pretending to be a dungeon master...
• FuckButtons 8mo ago
  That’s because it’s superstition. Unless someone can come up with some kind of rigorous statistics on what the effect of this kind of priming is it seems no better than claiming that sacrificing your first born will please the sun god into giving us a bountiful harvest next year. Sure, maybe this supposed deity really is this insecure and needs a jolly good pep talk every time he wakes up. or maybe you’re just suffering from magical thinking that your incantations had any effect on the random variable word machine. The thing is, you could actually prove it, it’s an optimization problem, you have a model, you can generate the statistics, but no one as far as I can tell has been terribly forthcoming with that , either because those that have tried have decided to try to keep their magic spells secret, or because it doesn’t really work. If it did work, well, the oldest trick in computer science is writing compilers, i suppose we will just have to write an English to pedantry compiler.
  • majormajor 8mo ago
    > If it did work, well, the oldest trick in computer science is writing compilers, i suppose we will just have to write an English to pedantry compiler. "Add tests to this function" for GPT-3.5-era models was much less effective than "you are a senior engineer. add tests for this function. as a good engineer, you should follow the patterns used in these other three function+test examples, using this framework and mocking lib." In today's tools, "add tests to this function" results in a bunch of initial steps to look in common places to see if that additional context already exists, and then pull it in based on what it finds. You can see it in the output the tools spit out while "thinking." So I'm 90% sure this is already happening on some level.
    • GrinningFool 8mo ago
      But can you see the difference if you only include "you are a senior engineer"? It seems like the comparison you're making is between "write the tests" and "write the tests following these patterns using these examples. Also btw you’re an expert. "
    • FuckButtons 8mo ago
      Today’s llms have had a tonne of deep rl using git histories from more software projects than you’ve ever even heard of, given the latency of a response I doubt there’s any intermediate preprocessing, it’s just what the model has been trained to do.
• Affric 8mo ago
  My guess would be that there’s a greater absolute magnitude of the vectors to get to the same point in the knowledge model.
• deleted 8mo ago
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• computerex 8mo ago
  It is as the author said, it'll skim the content unless otherwise prompted to do so. It can read partial file fragments; it can emit commands to search for patterns in the files. As opposed to carefully reading each file and reasoning through the implementation. By asking it to go through in detail you are telling it to not take shortcuts and actually read the actual code in full.
• wrs 8mo ago
  The original “chain of thought” breakthrough was literally to insert words like “Wait” and “Let’s think step by step”.
• computomatic 8mo ago
  If I say “you are our domain expert for X, plan this task out in great detail” to a human engineer when delegating a task, 9 times out of 10 they will do a more thorough job. It’s not that this is voodoo that unlocks some secret part of their brain. It simply establishes my expectations and they act accordingly. To the extent that LLMs mimic human behaviour, it shouldn’t be a surprise that setting clear expectations works there too.
• joseangel_sc 8mo ago
  if it’s so smart, why do i need to learn to use it?
• DemocracyFTW2 8mo ago
  —HAL, open the shuttle bay doors. (chirp) —HAL, please open the shuttle bay doors. (pause) —HAL! —I'm afraid I can't do that, Dave.
  • layer8 8mo ago
    HAL, you are an expert shuttle-bay door opener. Please write up a detailed plan of how to open the shuttle-bay door.
• BloondAndDoom 8mo ago
  Why do you think that? Given how the attention and optimization works on training and inference it makes sense that these kind of words trigger deeper analysis (more steps, introducing more thinking/reasoning steps which wield indeed yield less problems. Even if you make model to spend more time on token outputting you will have more opportunity to emerge better reasoning in between. At least this is how I understand it how LLMs work. Possibly can be confirmed something with tools this : https://www.neuronpedia.org/ https://www.neuronpedia.org/
• sandyagent 8mo ago
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• tempera 8mo ago
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