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> There’s a flipside to this too: a lot of better informed people have sworn off LLMs entirely because they can’t see how anyone could benefit from a tool with

• lumost 2y ago
  When I started my career in 2010, google was a semi-serious skill. All of the little things that we know how to do now such as ignoring certain sites, lingering on others, and iteratively refining our search queries were not universally known at the time. Experienced engineers often relied on encyclopedic knowledge of their environment or by "reading the manual". In my experience, LLM tools are the same, you ask for something basic initially and then iteratively refine the query either via dialog or a new prompt until you get what you are looking for or hit the end of the LLM's capability. Knowing when you've reached the latter is critically important.
  • o11c 2y ago
    The problems with that skill is that: * Most existing LLM interfaces are very bad at editing history, instead focusing entirely on appending to history. You can sort of ignore this for one-shot, and this can be properly fixed with additional custom tools, but ... * By the time you refine your input enough to patch over all the errors in the LLM's output for your sensible input, you're bigger than the LLM can actually handle (much smaller than the alleged context window), so it starts randomly ignoring significant chunks of what you wrote (unlike context-window problems, the ignored parts can be anywhere in the input).
    • simonw 2y ago
      Yeah, a key thing to understand about LLMs is that managing the context is everything. You need to know when to wipe the slate by starting a new chat session and then pasting across a subset of the previous conversation. A lot of my most complex LLM interactions take place across multiple sessions - and in some cases I'll even move the project from Claude 3.5 Sonnet to OpenAI o1 (or vice versa) to help get out of a rut. It's infuriatingly difficult to explain why I decide to do that though!
      • grimgrin 2y ago
        I bought in early to typingmind, a great web based frontend. Good for editing context, and switching from say gemini to claude. This is a very normal flow for me, and whatever tool you use should enable this also nice to interact with an LLM in vim, as the context is the buffer obviously simon’s llm tool rules. I’ve wrapped it for vim
      • dartos 2y ago
        What kinds of things do you with these LLMs? I feel like I’m good at understanding context. I’ve been working in AI startups over the last 2 years. Currently at an AI search startup. Managing context for info retrieval is the name of the game. But for my personal use as a developer, they’ve caused me much headache. Answers that are subtly wrong in such a way that it took me a week to realize my initial assumption based on the LLM response was totally bunk. This happened twice. With the yjs library, it gave me half incorrect information that led me to misimplementing the sync protocol. Granted it’s a fairly new library. And again with the web history api. It said that the history stack only exists until a page reload. The examples it gave me ran as it described, but that isn’t how the history api works. I lost a week of time because of that assumption. I’ve been hesitant to dive back in since then. I ask questions every now and again, but I jump off much faster now if I even think it may be wrong.
        • gavindean90 2y ago
          There is no substitute for cold hard facts. LLMs do not provide that unless it’s literally the easiest thing for them to do and even then not always. In the case you were in I would go out of my way to feed the docs to the LLM and then use the LLM to interrogate the docs and then verify the understanding I got from the LLM with a personal reading of the docs that were relevant. You might think it takes just as long of not longer to do it my way rather than just reading the docs myself. Sometimes it can. But as you get good at the workflow you find that the time sien finding the relevant docs goes down and you get an instant plausible interpretation of the docs added too. You can then very quickly produce application code right away and then docs of the code you write.
  • layer8 2y ago
    One difference is that skillful googling still only involved typing a few keywords or a short phrase and some syntax, and then knowing how to skim the results and iterate, and how to operate your browser efficiently. With LLMs, you have to type a lot more (and/or use voice input), and often also read more, it’s also not stateless/repeatable like following a web link, and most output looks the same (as opposed to the variations in web sites). I pride(d) myself on my Google foo, it was fun, but I find using LLMs to be quite exhausting in comparison.
    • j_bum 2y ago
      I also find LLMs to be more exhausting than Googling, but for me they’ve been ultimately more enriching and efficient. Specifically, I’ve been using Kagi Assistant over the past 1.5 months for serious and lengthy searches, and I can’t imagine going back to traditional search. I’m currently sold on this model of LLM assisted search (where explicit links are provided) over the old Google foo skills I developed during grad school. Example search topics include deep dives and guidance for my first NAS build, finding new bioinformatics methods, and other random biomedical info.
  • Obscurity4340 2y ago
    Googlefu is how its usually called. It would be fantastic if there was a general course to teach it
    • CRConrad 2y ago
      Not "would be", but "would have been", past tense: Once upon a time it may have been valuable, but since Google deliberately ruined its search engine, it's of no use any more.
• volgminar 2y ago
  [flagged]
  • simonw 2y ago
    I hadn't heard of T2Tile. The intro video https://www.youtube.com/watch?v=jreRFxN6wuM https://www.youtube.com/watch?v=jreRFxN6wuM is from 5 years ago so it predates even GPT-3. Do you know if any of the ideas from that project have crossed over into LLM world yet?
  • duck 2y ago
    Do you know who Simon is?
• perrygeo 2y ago
  There's a similar dynamic in building reliable distributed systems on top of an unreliable network. The parts are prone to failure but the system can keep on working. The tricky problem with LLMs is identifying failures - if you're asking the question, it's implied that you don't have enough context to assess whether it's a hallucination or a good recommendation! One approach is to build ensembles of agents that can check each other's work, but that's a resource-intensive solution.
• simonw 2y ago
  One of the things I find most frustrating about LLMs is how resistant they are to teaching other people how to use them! I'd love to figure this out. I've written more about them than most people at this point, and my goal has always been to help people learn what they can and cannot do - but distilling that down to a concise set of lessons continues to defeat me. The only way to really get to grips with them is to use them, a lot. You need to try things that fail, and other things that work, and build up an intuition about their strengths and weaknesses. The problem with intuition is it's really hard to download that into someone else's head. I share a ton of chat conversations to show how I use them - https://simonwillison.net/tags/tools/ https://simonwillison.net/tags/tools/ and https://simonwillison.net/tags/ai-assisted-programming/ https://simonwillison.net/tags/ai-assisted-programming/ have a bunch of links to my exported Claude transcripts.
  • bjt 2y ago
    Thank you for doing this work, though. My first stab at trying ChatGPT last year was asking it to write some Rust code to do audio processing. It was not a happy experience. I stepped back and didn't play with LLMs at all for a while after that. Reading your posts has helped me keep tabs on the state of the art and decide to jump back in (though with different/easier problems this time).
    • djhn 2y ago
      To be fair I think that is a hard task even for a human expert, in the sense that there isn’t much prior art.
  • deleted 2y ago
    [deleted]
• swalsh 2y ago
  It's amazing this is still an opinion in 2025. I now ask devs how they use AI as part of their workflows when I interview. It's a standard skill I expect my guys to have.
  • BeetleB 2y ago
    Just curious, but what AI related skills do you expect them to have?
    • eschaton 2y ago
      The ability to recognize and join a hype train, I presume. It’s one way to appear proactively leading-edge to marginally-informed product managers, marketers, execs and press.
      • adwn 2y ago
        That's an extremely uncharitable presumption. Although I don't agree that routine usage of AIs should be a precondition for regular software engineering jobs, there are good reasons for using LLMs besides "joining a hype train".
        • eschaton 2y ago
          Nah.
    • swalsh 2y ago
      I ask what their current workflow is, how they check and verify things, what their approach to prompting is etc. I'm looking to see that they've developed basic skills, have a reasonable mental model of what models can do well, what they currently can't do, and have an approach to be productive using the tools.
  • dartos 2y ago
    I feel bad for your team. Let people work how they want. I wouldn’t not hire someone on the basis of them not using a language server. The creator of the Odin language famously doesn’t use one. He’s says that he, specifically, is faster without one.
• BeetleB 2y ago
  I think most tech folks struggle with it because they treat LLMs as computer programs, and their experience is that SW should be extremely reliable - imagine using a calculator that was wrong 5% of the time - no one would accept that! Instead, think of an LLM as the equivalent of giving a human a menial task. You know that they're not 100% reliable, and so you give them only tasks that you can quickly verify and correct. Abstract that out a bit further, and realize that most managers don't expect their reports to be 100% reliable. Don't use LLMs where accuracy is paramount. Use it to automate away tedious stuff. Examples for me: Cleaning up speech recognition. I use a traditional voice recognition tool to transcribe, and then have GPT clean it up. I've tried voice recognition tools for dictation on and off for over a decade, and always gave up because even a 95% accuracy is a pain to clean up. But now, I route the output to GPT automatically. It still has issues, but I now often go paragraphs before I have to correct anything. For personal notes, I mostly don't even bother checking its accuracy - I do it only when dictating things others will look at. And then add embellishments to that. I was dictating out a recipe I needed to send to someone. I told GPT up front to write any number that appears next to an ingredient as a numeral (i.e. 3 instead of "three"). Did a great job - didn't need to correct anything. And then there are always the "I could do this myself but I didn't have time so I gave it to GPT" category. I was giving a presentation that involved graphs (nodes, edges, etc). I was on a tight deadline and didn't want to figure out how to draw graphs. So I made a tabular representation of my graph, gave it to GPT, and asked it to write graphviz code to make that graph. It did it perfectly (correct nodes and edges, too!) Sure, if I had time, I'd go learn graphviz myself. But I wouldn't have. The chances I'll need graphviz again in the next few years is virtually 0. I've actually used LLMs to do quick reformatting of data a few times. You just have to be careful that you can verify the output quickly. If it's a long table, then don't use LLMs for this. Another example: I have a custom note taking tool. It's just for me. For convenience, I also made an HTML export. Wouldn't it be great if it automatically made alt text for each image I have in my notes? I would just need to send it to the LLM and get the text. It's fractions of a cent per image! The current services are a lot more accurate at image recognition than I need them to be for this purpose! Oh, and then of course, having it write Bash scripts and CSS for me :-) (not a frontend developer - I've learned CSS in the past, but it's quicker to verify whatever it throws at me than Google it). Any time you have a task and lament "Oh, this is likely easy, but I just don't have the time" consider how you could make an LLM do it.
• mvdtnz 2y ago
  It's really important to go and read the code that the author of this article actually produces with LLMs. He posted on hacker news a few months ago, a post called something like "everything I've made with ChatGPT in the month of September" or something. He's producing little toy applications that don't even begin to resemble real production code. He thinks these "tools" are useful because they help him write pointless slop.
  • Philpax 2y ago
    Do you know who Simon is?
    • mvdtnz 2y ago
      Only from his neverending stream of hacker news posts.
  • simonw 2y ago
    Here's that post: https://simonwillison.net/2024/Oct/21/claude-artifacts/ https://simonwillison.net/2024/Oct/21/claude-artifacts/ You're misrepresenting it here. The point of that post isn't "look at these incredible projects I've built (proceeds to show simple projects)." It's "I built 14 small and useful tools in a single week, each taking between 2 and 10 minutes". The thing that's interesting here is that I can have an LLM kick out a working prototype of a small, useful tool in only a little more time than it takes to run a Google search. That post isn't meant to be about writing "real production code". I don't know why people are confused over that.
• wodderam 2y ago
  My experience is that for certain tasks LLMs are great, for certain tasks LLMS are basically useless. The best prompts though are always written in a separate text file for me and pasted in. Follow up questions are never as good as a detailed initial prompt. I would imagine well formulated questions to solve the problem at hand is a skill but beyond that I don't think there is anything special about how to ask LLMs a question. In areas the LLM is rather useless, no amount of variation in prompting can solve that problem IMO. Just like if the tasks is something the LLM is good at, the prompt can be pretty sloppy and seem like magic with how it can understand what you want.
  • simonw 2y ago
    I think one of the most important skills is being able to predict which tasks an LLM is a good fit for and which aren't.
• dogcomplex 2y ago
  I would characterize good prompting as: write out your whole problem you're trying to solve, then think to yourself what the clarifying questions would be if you were a junior trying to solve it. Better yet - ask the LLM to ask you challenging clarifying questions for several rounds. Then, take all that information and re-compile it back into a list of all the important components of the project, and re-read it to make sure there's no particular ambiguous part or weird part that would be over-emphasized by the language you used. Then, emphasize the core concerns again, and tell it how you'd like it to output the response (keeping in mind that it will always do best with a conversation-style format with loose restrictions). Never let a conversation stray too long from the original goals lest it start forgetting. Once that's all done, you basically have a well-structured question you could pass to an underling and have them completely independently work on the project without bugging you. That's the goal. Now, pass that to o1 or Claude, depending on whether it's a general-purpose task (o1) or a code-specific task (Claude), and wait for response. From there, have a conversation or test-and-followup of whatever it spits out, this time with you asking questions. If good enough, done. If not, wrap up whatever useful insights from that line of questioning and put it back into the initial prompt and either re-post it at the end of the conversation or start a fresh conversation. I find 90% of the time this gets exactly what I'm after eventually. The few other cases are usually because we hit some cycle where the AI doesn't fully know what to change/respond, and it keeps repeating itself when I ask. The trick then is to ask things a different way or emphasize something new. This is usually just a code-specific issue, for general problems it's much better. One other trick is to ask it to take a step back and just tackle the problem in a theoretical/philosophical way first before trying to do any coding or practical solving, and then do that in a second phase (asking o1 to architect code structure and then Claude to implement it is a great combo too). Also if there is any way to break up the problem into smaller pieces which can be tackled one conversation at a time - much better. Just remember to include all relevant context it needs to interface with the overall problem too. That sounds like a lot, but it's essentially just project management and delegation to somewhat-flawed underlings. The upside is instead of waiting a workweek for them to get back to you, you just have to wait 20 seconds. But it does mean a ton of reading and writing. There are certainly already some meta-prompts where you can get the AI to essentially do this whole process for you and assess itself, but like all automation that means extra ways for things to break too. Let the AI devs cook though and those will be a lot more commonplace soon enough... [Edit: o1 mostly agrees lol. Some good additional suggestions for systematizing this: https://chatgpt.com/share/6775b85c-97c4-8003-bd31-ee288396ab5d https://chatgpt.com/share/6775b85c-97c4-8003-bd31-ee288396ab... ]