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I feel like a danger with this sort of thing is that the capability of the system to use the right skill is limited by the little blurb you give about what the
by Imnimo 1y ago
I feel like a danger with this sort of thing is that the capability of the system to use the right skill is limited by the little blurb you give about what the skill is for. Contrast with the way a human learns skills - as we gain experience with a skill, we get better at understanding when it's the right tool for the job. But Claude is always starting from ground zero and skimming your descriptions.
- j45 1y agoLLMs are a probability based calculation, so it will always skim to some degree, and always guess to some degree, and often pick the best choice available to it even though it might not be the best. For folks who this seems elusive for, it's worth learning how the internals actually work, helps a great deal in how to structure things in general, and then over time as the parent comment said, specifically for individual cases.
- zobzu 1y agoIMO this is a context window issue. Humans are pretty good are memorizing super broad context without great accuracy. Sometimes our "recall" function doesn't even work right ("How do you say 'blah' in German again?"), so the more you specialize (say, 10k hours / mastery), the better you are at recalling a specific set of "skills", but perhaps not other skills. On the other hand, LLMs have a programatic context with consistent storage and the ability to have perfect recall, they just don't always generate the expected output in practice as the cost to go through ALL context is prohibitive in terms of power and time. Skills.. or really just context insertion is simply a way to prioritize their output generation manually. LLM "thinking mode" is the same, for what it's worth - it really is just reprioritizing context - so not "starting from scratch" per se. When you start thinking about it that way, it makes sense - and it helps using these tools more effectively too.
- dwaltrip 1y agoThere are ways to compensate for lack of “continual learning”, but recognizing that underlying missing piece is important.
- ryancnelson 1y agoI commented here already about deli-gator ( https://github.com/ryancnelson/deli-gator https://github.com/ryancnelson/deli-gator ) , but your summary nailed what I didn’t mention here before: Context. I’d been re-teaching Claude to craft Rest-api calls with curl every morning for months before i realized that skills would let me delegate that to cheaper models, re-using cached-token-queries, and save my context window for my actual problem-space CONTEXT.
- dingnuts 1y ago>I’d been re-teaching Claude to craft Rest-api calls with curl every morning for months what the fuck, there is absolutely no way this was cheaper or more productive than just learning to use curl and writing curl calls yourself. Curl isn't even hard! And if you learn to use it, you get WAY better at working with HTTP! You're kneecapping yourself to expend more effort than it would take to just write the calls, helping to train a bot to do the job you should be doing
- jmtulloss 1y agoMy interpretation of the parent comment was that they were loading specific curl calls into context so that Claude could properly exercise the endpoints after making changes.
- F7F7F7 1y agoHe’s likely talking about Claude’s hook system that Anthropic created to provide better control over context.
- ryancnelson 1y agoi know how to use curl. (I was a contributor before git existed) … watching Claude iterate to re-learn whether to try application/x-form-urle ncoded or GET /?foo wastes SO MUCH time and fills your context with “how to curl” that you re-send over again until your context compacts. You are bad at reading comprehension. My comment meant I can tell Claude “update jira with that test outcome in a comment” and, Claude can eventually figure that out with just a Key and curl, but that’s way too low level. What I linked to literally explains that, with code and a blog post.
- mbesto 1y ago> IMO this is a context window issue. Not really. It's a consequential issue. No matter how big or small the context window is, LLMs simply do not have the concept of goals and consequences. Thus, it's difficult for them to acquire dynamic and evolving "skills" like humans do.
- adastra22 1y agoWorth noting, even though it isn’t critical to your argument, that LLMs do not have perfect recall. I got to great lengths to keep agentic tools from relying on memory, because they often get it subtly wrong.
- seunosewa 1y agoThe blurbs can be improved if they aren't effective. You can also invoke skills directly. The description is equivalent to your short term memory. The skill is like your long term memory which is retrieved if needed. These should both be considered as part of the AI agent. Not external things.
- blackoil 1y agoMost of the experience is general information not specific to project/discussion. LLM starts with all that knowledge. Next it needs a memory and lookup system for project specific information. Lookup in humans is amazingly fast, but even with a slow lookup, LLMs can refer to it in near real-time.
- andruby 1y agoWould this requirement to start from ground zero in current LLMs be an artefact of the requirement to have a "multi-tenant" infrastructure? Of course OpenAI and Anthropic want to be able to reuse the same servers/memory for multiple users, otherwise it would be too expensive. Could we have "personal" single-tenant setups? Where the LLM incorporates every previous conversation?
- mbesto 1y ago> Contrast with the way a human learns skills - as we gain experience with a skill, we get better at understanding when it's the right tool for the job. Which is precisely why Richard Sutton doesn't think LLMs will evolve to AGI[0]. LLMs are based on mimicry, not experience, so it's more likely (according to Sutton) that AGI will be based on some form of RL (reinforcement learning) and not neural networks (LLMs). More specifically, LLMs don't have goals and consequences of actions, which is the foundation for intelligence. So, to your point, the idea of a "skill" is more akin to a reference manual, than it is a skill building exercise that can be applied to developing an instrument, task, solution, etc. [0] https://www.youtube.com/watch?v=21EYKqUsPfg https://www.youtube.com/watch?v=21EYKqUsPfg
- buildbot 1y agoThe industry has been doing RL on many kinds of neural networks, including LLMs, for quite some time. Is this person saying we RL on some kind of non neural network design? Why is that more likely to bring AGI than an LLM?. > More specifically, LLMs don't have goals and consequences of actions, which is the foundation for intelligence. Citation?
- jfarina 1y agoWhy are you asking them to cite something for that statement? Are you questioning whether it's the foundation for intelligence or whether LLMS understand goals and consequences?
- ChadMoran 1y agoThis is the crux of knowledge/tool enrichment in LLMs. The idea that we can have knowledge bases and LLMs will know WHEN to use them is a bit of a pipe dream right now.
- fragmede 1y agoCan you be more specific? The simple case seems to be solved, eg if I have an mcp for foo enabled and then ask about a list of foo, Claude will go and call the list function on foo.
- corytheboyd 1y ago> […] and then ask about a list of foo Not OP, but this is the part that I take issue with. I want to forget what tools are there and have the LLM figure out on its own which tool to use. Having to remember to add special words to encourage it to use specific tools (required a lot of the time, especially with esoteric tools) is annoying. I’m not saying this renders the whole thing “useless” because it’s good to have some idea of what you’re doing to guide the LLM anyway, but I wish it could do better here.
- fragmede 1y agoI've got a project that needs to run a special script and not just "make $target" at the command line in order to build, and with instructions in multiple . MD files, codex w/ gpt-5-high still forgets and runs make blindly which fails and it gets confused annoyingly often. ooh, it does call make when I ask it to compile, and is able to call a couple other popular tools without having to refer to them by name. if I ask it to resize an image, it'll call imagemagik, or run ffmpeg and I don't need to refer to ffmpeg by name. so at the end of the day, it seems they are their training data, so better write a popular blog post about your one-off MCP and the tools it exposes, and maybe the next version of the LLM will have your blog post in the training data and will automatically know how to use it without having to be told
- delaminator 1y ago
- larrymcp 1y ago> starting from ground zero You probably mean "starting from square one" but yeah I get you
- ex3ndr 1y agoHumans dont need a skill to know that they need a skill
- SebastianSosa1 1y agoExcellent point, put simply building those preferences and lessons would demand a layer of latent memory, personal models, maybe now is a good time to revisit this idea...
- RicDan 1y agoSkills are literally technical documentation for your project it seems. So now we can finally argue for time to write doc, just name it "AI enhancing skill definitions"