What I'm Finding About LLM Code Style and Token Costs(www.jimmont.com)
29 pointsby jimmont7 hours ago8 comments
  • bombcar4 hours ago
    I just had Claude try to process an RSS feed and it was about to ZALGΌ IS TOƝȳ THË PO NY itself and I pointed that out and it immediately said "Wordpress has a json interface, I'll use that".

    You need to know the shape of the solution ...

    • anttiharju4 hours ago
      Context about tony the pony

      https://stackoverflow.com/questions/1732348/regex-match-open...

      • Balinaresan hour ago
        Thank you for helpfully de-darmoking that for the less pony-attuned of us.
    • vadansky4 hours ago
      If feels like the photoshop paint bucket tool.

      If you draw a sloppy circle and fill it in, it'll "escape" and try to paint the whole canvas (and back in the day would get my slow computer stuck until I spam "esc").

      You have to be able to draw a good circle to use it.

  • lelanthran41 minutes ago
    It's really hard for me to parse LLM-generated prose in blog posts - the reasoning is disjointed, logic is split everywhere.

    Is it already too late to have humans just write down what they are thinking instead of passing it through a lossy expander?

  • datadrivenangel4 hours ago
    The code comments are an especially brutal thing to add cruft and bloat and confuse the coding agents.

    And it feels like claude code has gotten more verbose with the multiline comments lately

  • Izkata2 hours ago
    The "Form data" section is doing two completely different things, the large one is two different implementations of a React component mixed together while the short one doesn't store "data" anywhere for use later like the React one does.

    Edit: Similar with the "UI components" section, the long one is missing the UI while the short one is UI without the trigger to activate it. You'd probably combine the two, using state from the first to control the UI in the second (replacing the contents of the useEffect with the dialog API to get the modal effect).

  • 3 hours ago
    undefined
  • ftaisdeal6 hours ago
    Excellent article, with impeccable analysis, that will fundamentally change how I work with Claude myself. I have already learned to give Claude both a "do" and a "don't" in order to limit unpleasant surprises.
  • jimmont7 hours ago
    Reviewing my experience using LLMs, to improve results, reduce churn and token usage. Discovering the gap between what they produce and what I'd normally do is a significant source of output cost, regressions and surfacing a bit of why and how to fix it. Notably Claude is remarkably bad at/about this, producing errors even when directed toward modern Web solutions—that cut token use a lot, like toward 90% occasionally, which together with the frustrating churn led me to review how I'm working, what is happening and generate this article.
  • defytonofficial6 hours ago
    This matches my experience. I've been using OpenRouter with GPT-4o for an image verification service, and the prompt engineering choices have a measurable impact on cost.

    One thing I found: asking the model to respond in structured JSON (with a strict schema) vs free-form text cuts token output by ~40% on average. The model stops "explaining itself" and just gives you the answer.

    Also noticed that including a reference image in vision calls roughly doubles the input cost but improves accuracy enough that you save on retries. Net cost ended up lower for my use case.

    Curious if you've measured the difference between asking for "concise" output vs actually constraining the response format.

    • jimmontan hour ago
      That's an excellent idea I plan to try, thanks—re using structured JSON with schema. The most success I've had is saying "be brief" or an explicit size, like one line, or do not explain, etc. I haven't measured other instructions so extensively. They do work but the more specific the better. Other strategies around outputs that are more natural language seem to be hands-down the direction to take, and get away from the machine language habits we've used in the past. It's super interesting seeing this new practice emerging and more or less inventing parts of it along the way. Right now I'm at the place where my brute force and elaborate explanations were reaching their limit and in the frustration just realized I need to take a few days and try to figure out the tool. Across all these the pattern seems entirely that the constraints bound the probability space, whether it's the format like you suggested, or the instruction we give, including the space we point it toward (Web APIs, runtime, schema, etc). In all instances where it's not working the solution seems to be what does the pattern reduce to, and what specifics are the do/don't to go with that, and most of the time the results improve immediately. Your tip seems excellent for this. An easy-button.
    • linzhangrun2 hours ago
      why still use gpt-4o?