A mistake I see people repeating over and over, is never restarting their conversations with a edited initial message.
Instead of doing what the author is doing here, and sending messages back and forward, leading to a longer and longer conversation, where each messages leads to worse and worse quality replies, until the LLM seems like a dumb rock, rewrite your initial message with everything that went wrong/was misunderstood, and aim to have whatever you want solved in the first message, and you'll get a lot higher quality answers. If the LLM misunderstood, don't reply "No, what I mean was..." but instead rewrite the first message so it's clearer.
This is at least true for all ChatGPT, Claude and DeepSeek models, YMMV with other models.
Inasmuch as these are collaborative document generators at their core, "minimally ambiguous prompt and conforming reply" is a strongly represented document structure and so we benefit by setting them up to complete one.
Likewise, "tragi-comic dialog between increasingly frustrated instructor and bumbling pupil" is also a widely represented document structure that we benefit by trying to avoid.
Chatbot training works to minimize the chance of an LLM engaging in the latter, because dialog is a intuitive interface that users enjoy, but we can avoid the problem more successfully by just providing a new and less ambiguous prompt in a new session, as you suggest.
This means the leading UI for LLMs - the chat - is the wrong UI, at least for some of the tasks. We should instead have a single query text field, like in search engines, that you continue to edit and refine, just like in complex search queries.
I like zed's approach, where the whole discussion is a plain text file you can edit as any text, which gives you the ability to change anything in the "discussion" regardless if it was generated by you or the llm. It makes stuff like that much simpler, ie you can correct simple stuff in the llm's response without unecessary back and forths, you can just cut parts out of the discussion to reduce context size or guide the discussion where you actually want removing distractions etc. I don't understand why the dominant approach is an actual, realistic chat interface where you can only add a new response, or in best case create "threads".
> I don't understand why the dominant approach is an actual, realistic chat interface where you can only add a new response, or in best case create "threads".
I'm not 100% sure either, I think it might just be a first-iteration UX that is generally useful, but not specifically useful for use cases like coding.
To kind of work around this, I generally keep my prompts as .md files on disk, treat them like templates where I have variables like $SRC that gets replaced with the actual code when I "compile" them. So I write a prompt, paste it into ChatGPT, notice something is wrong, edit my template on disk then paste it into a new conversation. Iterate until it works. I ended up putting the CLI I use for this here, in case others wanna try the same approach: https://github.com/victorb/prompta
I wonder if people in general would have a healthier understanding of LLMs if this mode of interaction was more common. Perhaps it would be more clear that the LLM is a very souped up autocomplete, instead of some other mind to debate.
Yes I think the same. It really felt demystifying the experience for me when I first tried zed, and I already had that belief about LLMs. But when you use an LLM through a normal chat interface, it feels different, and they are also tuned to feel that way, like talking to somebody. Maybe this is why this approach is avoided by those who have some stake on AI, even if it is better UX.
I've found the most useful LLM UIs for me are tree-like with lots of branches where you go back and forth between your prompts. You branch off anywhere, edit top or leafs as you go.
If one branch doesn't work out you go back to the last node that gave good results or the top and create another branch with a different prompt from.
Or if you want to ask something in a different direction but don't want all the baggage from recent nodes.
I still think there is value in chats and retaining context. But there is also value in starting clean when necessary. Giving users control and teaching people how to use it is the way IMO.
The problem with retaining context is that it gets polluted. That pollution gets you into a latent space with errors, which probably not where you want your next token prediction to be sourced.
The reasonable alternative is a chat interface that lets you edit any text, the AI response or your prompts, and regenerate from any point. This is why I use the API "playground" interfaces or something like LibreChat. Deepseek at least has prompt editing/regeneration.
> This means the leading UI for LLMs - the chat - is the wrong UI
For coding, I'd agree. But seemingly people use LLMs for more than that, but I don't have any experience myself. But I agree with the idea that we haven't found the right UX for programming with LLMs yet. I'm getting even worse results with Aider, Cursor and all of those, than just my approach outlined above, so that doesn't seem like the right way either.
I've also started adding "Ask any questions you think are relevant before starting" to the end of my prompts. It usually results in at least one question that addresses something I didn't think to add to my prompt.
It seems like the author in fact did do this. They asked Claud the same message. I really doubt they repeated the entire conversation to get to that point, but I may be wrong.
From personal experience, I agree with you, but I wouldn't make the critique here as it is far from a magic bullet. Honestly, with the first stuff it seems faster to learn mermaid and implement it yourself. Mermaid can be learned in a rather short time, the basic syntax is fairly trivial and essentially obvious. As an added benefit, you then get to have this knowledge and use it later on. This will certainly feel slower than the iterative back and forth with a LLM -- either by follow-up conversations or refining your one shot -- but I'm not convinced it will be a huge difference in time as measured by the clock on the wall[0]
[0] idk, going back and forth with an LLM and refining my initial messages feels slow to me. It reminds me of print statement debugging in a compiled language. Lots of empty time.
It doesn't seem like that to me. At one point in the article: "There are also a few issues [...] Let’s fix with the prompt" and then a prompt that is referring the previous message. Almost all prompts after that seem to depend on the context before them.
My point is that instead of doing that, revise the original initially message so the very first response from the LLM doesn't contain any errors, because (in my experience) that's way easier and faster than trying to correct errors by adding more messages, since they all (even O1 Pro) seem to lose track of what's important in the conversation really fast.
I built Plandex[1], an open source AI coding agent, partly to enable this workflow.
It has `log` and `rewind` commands that allow you to easily back up to any previous point in the conversation and start again from there with an updated prompt. Plandex also has branches, which can be helpful for not losing history when using this approach.
You’re right that it’s often a way to get superior results. Having mistakes or bad output in the conversation history tends to beget more mistakes and bad output, even if you are specifically directing the LLM to fix those things. Trial and error with a new prompt and clean context avoids this problem.
I tried this approach when attempting to get Deepseek-r1 and GrokV3 to create a simple CUDA application. It was necessary because the iterative approach kept leading to hangs and divergent behaviors. I still wasn't able to get a working application, however.
I love Claude, but whomever works on their UI needs to be slapped a bit. Code output covering the stop button on my laptop, page lockups on iPhone/Chrome with certain artifacts (even after reload), crazy slow typing on the computer and refusal to “continue” chat with a cheaper model. Simply providing a summary of the chat on running out of tokens would let me start another conversation, or at least a warning I was getting close.
That’s too much work. I’d rather ask the LLM to rewrite my first message for me. And the UI should then give me an option to “start new chat from suggested prompt.”
> I’d rather ask the LLM to rewrite my first message for me
I guess you can do that too, as long as you start a new conversation afterwards. Personally I found it much easier to keep prompts in .md files on disk, and paste them into the various interfaces when needed, and then I iterate on my local files if I notice the first answer misunderstood/got something wrong. Also lets you compose prompts which is useful if you deal with many different languages/technologies and so on.
Instead of doing what the author is doing here, and sending messages back and forward, leading to a longer and longer conversation, where each messages leads to worse and worse quality replies, until the LLM seems like a dumb rock, rewrite your initial message with everything that went wrong/was misunderstood, and aim to have whatever you want solved in the first message, and you'll get a lot higher quality answers. If the LLM misunderstood, don't reply "No, what I mean was..." but instead rewrite the first message so it's clearer.
This is at least true for all ChatGPT, Claude and DeepSeek models, YMMV with other models.