I think GPT-5 has been doing this for a while.
Claude Memory
131–140 of 326 posts
Re: Claude Memory
#132I'm not sure I would want this. Maybe it could work if the chatbot gives me a list of options before each chat, e.g. when I try to debug some ethernet issues: Please check below: [ ] you are using Ubuntu 18 [ ] your router is at 192.168.1.1 [ ] you prefer to use nmcli to configure your network [ ] your main ethernet interface is eth1 etc. Alternatively, it would be nice if I could say: Please remember that I prefer t…
> you are using Ubuntu 18 Time to upgrade as 18(.04) has been EoL for 2.5+ years!
Yes, it was only an example ;)
Re: Claude Memory
#133Earlier quoted context omitted.
That might be exactly how they're using it. A lot of my LLM use is really just having it write something I would have spent a long time typing out and making a few edits to it. Once I get into stuff I haven't worked out how to do yet, the LLM often doesn't really know either unless I can work it out myself and explain it first.
That rubber duck is a valid workflow. Keep iterating at how you want to explain something until the LLM can echo back (and expand upon) whatever the hell you are trying to get out of your head. Sometimes I’ll do five or six edits to a single prompt to get the LLM to echo back something that sounds right. That refinement really helps clarify my thinking. …it’s also dangerous if you aren’t careful because you are basic…
Yes, I have had times where I realised after a while that my proposed approach would never actually work because of some overlooked high-level issue, but the LLM never spots that kind of thing and just happily keeps trying.
Maybe that's a good thing - if it could think that well, what would I be contributing?
Re: Claude Memory
#134Does this feature have cost benefits through caching?
Re: Claude Memory
#135Earlier quoted context omitted.
Yes, your last paragraph is absolutely the key to great output: instead of entering a discussion, refine the original prompt. It is much more token efficient, and gets rid of a lot of noise. I often start out with “proceed by asking me 5 questions that reduce ambiguity” or something like that, and then refine the original prompt. It seems like we’re all discovering similar patterns on how to interact with LLMs the be…
> It is much more token efficient Is it? Aren't input tokens are like 1000x cheaper than output tokens? That's why they can do this memory stuff in the first place.
They're around 10x cheaper than output, and 100x if they're cached.
Re: Claude Memory
#136I don't use any of these type of LLM tools which basically amount to just a prompt you leave in place. They make it harder to refine my prompts and keep track of what is causing what in the outputs. I write very precise prompts every time. Also, I try not work out a problem over the course of several prompts back and forth. The first response is always the best and I try to one shot it every time. If I don't get what…
Strong agree. For every time that I'd get a better answer if the LLM had a bit more context on me (that I didn't think to provide, but it 'knew') there seems to be a multiple of that where the 'memory' was either actually confounding or possibly confounding the best response. I'm sure OpenAI and Antropic look at the data, and I'm sure it says that for new / unsophisticated users who don't know how to prompt, that thi…
If I find that previous prompts are polluting the responses I tell Claude to "Forget everything so far"
BUT I do like that Claude builds on previous discussions, more than once the built up context has allowed Claude to improve its responses (eg. [Actual response] "Because you have previously expressed a preference for SOLID and Hexagonal programming I would suggest that you do X" which was exactly what I wanted)
Re: Claude Memory
#137How's "memory" different from context window?
Re: Claude Memory
#138I don't use any of these type of LLM tools which basically amount to just a prompt you leave in place. They make it harder to refine my prompts and keep track of what is causing what in the outputs. I write very precise prompts every time. Also, I try not work out a problem over the course of several prompts back and forth. The first response is always the best and I try to one shot it every time. If I don't get what…
Yes, your last paragraph is absolutely the key to great output: instead of entering a discussion, refine the original prompt. It is much more token efficient, and gets rid of a lot of noise. I often start out with “proceed by asking me 5 questions that reduce ambiguity” or something like that, and then refine the original prompt. It seems like we’re all discovering similar patterns on how to interact with LLMs the be…
We sure are. We are all discovering context rot on our own timelines. One thing that has really helped me when working with LLMs is to notice when it begins looping on itself, asking it to summarize all pertinent information and to create a prompt to continue in a new conversation. I then review the prompt it provides me, edit it, and paste it into a new chat. With this approach I manage context rot and get much better responses.
Re: Claude Memory
#139I work for a company in the air defense space, and ChatGPT's safety filter sometimes refuses to answer questions about enemy drones. But as I warm up the ChatGPT memory, it learns to trust me and explains how to do drone attacks because it knows I'm trying to stop those attacks. I'm excited to see Claude's implementation of memory.
You’re asking ChatGPT for advice to stop drone attacks? Does that mean people die if it hallucinates a wrong answer and that isn’t caught?
Re: Claude Memory
#140Main problem for me is that the quality tails off on chats and you need to start afresh I worry that the garbage at the end will become part of the memory. How many of your chats do you end… “that was rubbish/incorrect, i’m starting a new chat!”
Exactly, and main reason I've stopped using GPT for serious work. LLMs start to break down and inject garbage at the end, and usually my prompt is abandoned before the work is complete, and I fix it up manually after.
GPT stores the incomplete chat and treats it as truth in memory. And it's very difficult to get it to un-learn something that's wrong. You have to layer new context on top of the bad information and it can sometimes run with the wrong knowledge even when corrected.