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> AI are unteachable, if you have given them a good prompt and they do something wrong 90% of the time you are shit out of luck. If the Model makes repeated mi
by benjiro29 3mo ago
> AI are unteachable, if you have given them a good prompt and they do something wrong 90% of the time you are shit out of luck.
If the Model makes repeated mistakes on the same subject matter, you can update your agent.md file, or you can add skills to deal with specific prompts, or you provide a better default harness.
The whole idea of coding agents is their harness makes a big difference vs a pure raw model.
> However AI cannot meaningfully handle feedback and learn
How do you think models are created? They are trained on feedback and learn.
Its not cheap but you can post train models. This is how custom models are mode, that deal with specific tasks more efficiently and accurately.
Example ... Composer? Its base Kimi v2.5 model that has been post-trained 2 weeks, to create Composer 2.5, what is a much better coding model.
Its literally trained to make less mistakes by feeding it correct data. Hell, a lot of the models you are using, are often the same base model, where v2.0 was the initial released model but the model keeps training, so when they release v2.1, its still the same model, but with more training time on feedback provided to v2.0.
LLM Models are not a cake you cook one time and they are done, and you start from zero again. If you have the money, and a powerful server setup, you can take a model like GLM 5.2 and post-train it, to reduce specific errors. Sure, you need a ton of money because its a large model.
But people have been doing this with 5M, 100M, 1B, 5B models for a long time already. To the point that some of the small models can do specific tasks, almost or better then some of the huge more general trained models.
- rowanG077 3mo ago> If the Model makes repeated mistakes on the same subject matter, you can update your agent.md file ... That's all just prompting. > How do you think models are created? They are trained on feedback and learn. No one is post training models on a single mistake. At least I have not seen it. I also doubt it is effective. Post-training on a single failure will not meaningfully change the model. That even sidesteps the entire problem that you don't even have access to models if you use a provider like anthropic/openai
- brookst 3mo ago> That's all just prompting And telling someone not to repeat a mistake is… ?
- rowanG077 3mo agoAre you really making the case that teaching a person how to work is equivalent to prompting an AI?
- brookst 3mo agoYes. Tell me why it’s different without using circular reasoning?
- rowanG077 3mo agoThey're different mechanisms. Teaching modifies the learner. Prompting doesn't modify the model. It provides additional context that influences a single inference. A person who has learned something can apply it years later without being reminded. An LLM generally cannot unless the knowledge is incorporated into the model itself or provided again.
- SR2Z 3mo agoRight, but "provided again" is what SKILL.md or whatever else are for. The value of LLMs is that they're stateless. With sufficiently detailed documentation and a well-bounded task, they are quite useful.
- rowanG077 3mo agoI don't think it's generally thought of an advantage that LLMs are stateless. In deployment it's nice, it would be much better if the model could continually learn. I'm not claiming LLMs are not useful, they most certainly are.
- unparagoned 3mo ago
- throw1234567891 3mo ago> That's all just prompting. >> Joe, how many times have I asked you to pay attention to commas. Come on, "let's eat grandma".