3 ms·

Interesting! Do you have an example with code? I am struggling to adapt the cheeseburger example to something with code.

verdverm 2y ago
"Add RBAC to my application"
- numpad0 2y ago
  Kind of like that, I had to word the input so that instruction looked like collapsed code and apparent correlation can be seen. It didn't do right from mere description of an end result. The code was just an html file with some sticky buttons that would reset. AI left some stuffs set, left reset function empty, had handling codes scattered everywhere etc etc and just didn't get it. Being able to just keep rubberstamping AI until it breaks was a huge time saving, but it wasn't quite as much IQ saving.
  - squeegee_scream 2y ago
    With the example, "Add RBAC to my application", I’ve had success telling the LLM, “I want your help creating a plan to add RBAC to my application. I’m sending the codebase as context (or just the relevant parts if the entire codebase is too large). Please respond with a high-level, nontechnical outline of how we might do this.” Then take each step and recursively flesh it out until each step is thoroughly planned. It’s wise to get the LLM to think more broadly and more collaboratively with questions like, “What question should I ask you that I haven’t yet” and similar
    - verdverm 2y ago
      This gets at the point of of the original "cheeseburger" vs having to explain what a cheeseburger is to get a cheeseburger
TeMPOraL 2y ago
The example is kind of opposite of the point GP's trying to make, isn't it? I mean, "cheeseburger" is a very well-known and a very specific thing. You ask a cook to make you one, they'll know what you want. Try asking for "a hamburger with patty topped with sliced cheese and standard condiments", and you'll probably get back a confused look, "er, you mean you want a cheeseburger, right?". Same pattern happens with LLMs, in my experience. GP says an LLM infrerence is "sort of a decompression process for a lossy copy of the Internet" - but in these terms, if asking it for a cheeseburger means decompressing parts of the latent space around the term "cheeseburger", then asking for "a hamburger with patty topped with sliced cheese and standard condiments" is making it decompress much larger space around multiple terms, and then filter the result out into a semantically relevant subspace, and then run extra inference on that. If you think about it, the very reason we (humans) give names to things is to avoid having to repeatedly do that decompression and filtering every time we want to refer to a specific thing. We call the modified "hamburger" a "cheeseburger" precisely to avoid having to talk like GP suggests we should talk to LLMs, so I very much think this advice is backwards.
- numpad0 2y ago
  Cursor kept giving me burgers without meat, without top, double wrapped, etc. I started giving it nested bullet points with amount of details roughly represent gut-felt relative volume of functions, and then the agent started macro-expanding the instructions much better. It did relocate, rename, and reorganize functions as needed. Then I could ask for fixes, manually trim extra bits, filled unmarked TODOs, to reach the desired end result. It also seemed to do better with occasional stern instructions, like "ok but you're wrong, fix $item", than consistently teacher-student-like interactions like "Great! However, there is..." IME. I suppose students solutions are more likely to be wrong and/or less sophisticated. The end product is on the Internet, nothing secret or inappropriate... I'm just reluctant to post "my first HTML" on HN.

verdverm 2y ago

"Add RBAC to my application"

numpad0 2y ago
Kind of like that, I had to word the input so that instruction looked like collapsed code and apparent correlation can be seen. It didn't do right from mere description of an end result. The code was just an html file with some sticky buttons that would reset. AI left some stuffs set, left reset function empty, had handling codes scattered everywhere etc etc and just didn't get it. Being able to just keep rubberstamping AI until it breaks was a huge time saving, but it wasn't quite as much IQ saving.
- squeegee_scream 2y ago
  With the example, "Add RBAC to my application", I’ve had success telling the LLM, “I want your help creating a plan to add RBAC to my application. I’m sending the codebase as context (or just the relevant parts if the entire codebase is too large). Please respond with a high-level, nontechnical outline of how we might do this.” Then take each step and recursively flesh it out until each step is thoroughly planned. It’s wise to get the LLM to think more broadly and more collaboratively with questions like, “What question should I ask you that I haven’t yet” and similar
  - verdverm 2y ago
    This gets at the point of of the original "cheeseburger" vs having to explain what a cheeseburger is to get a cheeseburger

TeMPOraL 2y ago

The example is kind of opposite of the point GP's trying to make, isn't it? I mean, "cheeseburger" is a very well-known and a very specific thing. You ask a cook to make you one, they'll know what you want. Try asking for "a hamburger with patty topped with sliced cheese and standard condiments", and you'll probably get back a confused look, "er, you mean you want a cheeseburger, right?". Same pattern happens with LLMs, in my experience. GP says an LLM infrerence is "sort of a decompression process for a lossy copy of the Internet" - but in these terms, if asking it for a cheeseburger means decompressing parts of the latent space around the term "cheeseburger", then asking for "a hamburger with patty topped with sliced cheese and standard condiments" is making it decompress much larger space around multiple terms, and then filter the result out into a semantically relevant subspace, and then run extra inference on that. If you think about it, the very reason we (humans) give names to things is to avoid having to repeatedly do that decompression and filtering every time we want to refer to a specific thing. We call the modified "hamburger" a "cheeseburger" precisely to avoid having to talk like GP suggests we should talk to LLMs, so I very much think this advice is backwards.

numpad0 2y ago
Cursor kept giving me burgers without meat, without top, double wrapped, etc. I started giving it nested bullet points with amount of details roughly represent gut-felt relative volume of functions, and then the agent started macro-expanding the instructions much better. It did relocate, rename, and reorganize functions as needed. Then I could ask for fixes, manually trim extra bits, filled unmarked TODOs, to reach the desired end result. It also seemed to do better with occasional stern instructions, like "ok but you're wrong, fix $item", than consistently teacher-student-like interactions like "Great! However, there is..." IME. I suppose students solutions are more likely to be wrong and/or less sophisticated. The end product is on the Internet, nothing secret or inappropriate... I'm just reluctant to post "my first HTML" on HN.