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Comment by gazereth | Hacker News Reader

Personally I've found that you need to define the strategy yourself, or in a separate prompt, and then use a chain-of-thought approach to get to a good solution. Using the example you gave:

  Hey Chat,
  Write me some basic rust code to download a url. I'd like  to pass the url as an string argument to the file

Then test it and expand:

  Hey Chat,
  I'd like to pass a list of urls to this script and fetch them one by one. Can you update the code to accept a list of urls from a file?

Test and expand, and offer some words of encouragement:

  Great work chat, you're really in the zone today!

  The downloads are taking a bit too long, can you change the code so the downloads are asynchronous. Use the native/library/some-other-pattern for the async parts.

Test and expand...

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Whew, that's a lot to type out and you have to provide words of encouragement? Wouldn't it make more sense to do a simple search engine query for a HTTP library then write some code yourself and provide that for context when doing more complicated things like async?

I really fail to see the usefulness in typing out long winded prompts then waiting for information to stream in. And repeat...

A few options.

1. Use TTS and have an LLM clean it up.

2. Use a collection of prompt templates.

I meant VTT, not TTS

I'm going the exact opposite way. I provide all important details in the prompt and when I see that the LLM understood something wrong, I start over and add the needed information to the prompt. So the LLM either gets it on the first prompt, or I write the code myself. When I get the "Yes, you are right ..." or "now I see..." crap, I throw everything away, because I know that the LLM will only find shit "solutions".

This is actually a great approach. Essentially you're using time travel to prevent misunderstandings, which prevents the context from getting clogged up with garbage.

This is the best approach and avoids long context windows that get the LLM confused

I have heard a few times that "being nice" to LLMs sometimes improves their output quality. I find this hard to believe, but happy to hear your experience.

Examples include things like, referring to LLM nicely ("my dear"), saying "please" and asking nicely, or thanking.

Do these actually work?

I use to do the "hey chat" all the time out of habit and when I thought the language model was something more like AI in a movie than what it is. I am sure it makes no difference beyond the user acting different and possibly asking better questions if they think they are talking to a person. Now for me, it looks completely ridiculous.

Well consider it's training data. I could easily see questions on sites like stack overflow having better quality answers when the original question is asked nicely. I'm not sure if it's a real effect or not but I could see how it could be. A rudely asked question will have a lot of flame war responses.

I'm not sure encouragment itself is the performance enhancer, it's more that you're communicating that the model has the right "vibe" of what your end goal is.

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