61–70 of 112 posts
The suggestion to use markdown tables was quite interesting. It makes a lot of sense, and I haven't seen it described elsewhere. I have been getting good results by asking GPT to produce semi structured responses based on other aspects of (GitHub) markdown. In general, I find it very helpful to find an already popular format that suits your problem. The model is probably already fluent in rendering that output format…
Thanks for putting this together!
Earlier quoted context omitted.
I always thought this problem was better thought of as software engineer/farmer problem - it's easer to teach a software engineer about agriculture than the other way around
Now I'm really wondering if that's true or not. Software engineering can be self-studied very cheaply, lots of free resources, mostly you just need time and motivation. Failure is usually cheap. Farming on the other hand requires more local, implicit and hands-on knowledge, capital requirements are high, feedback cycles are slower and failure is expensive.
It’s much more difficult than software engineering, in every way.
Worringly, I am it sure the people working on this really understand what a Transformer is Quote from them: “ There is still active research in non-transformer based language models though, such as Amazon’s AlexaTM 20B which outperforms GPT-3“ Quote from said paper “ For AlexaTM 20B, we used the standard Transformer model architecture“ (Its just an encoder decoder transformer)
I just updated the guide: https://github.com/brexhq/prompt-engineering/commit/3a3ac17a...
Isn't engineering the application of science to solve problems? (math, definitive logic, etc.)
Maybe one day we'll have instruments that let us reason about the connections between prompts and the exact state of the AI, so that we can understand the mechanics of causation, but until then, I would not think that being good at asking questions is "engineering"
Are most 10 year olds veteran "search engineers"?
Btw I'm asking this slightly tongue-in-cheek, as a discussion point. For example plenty of computer system hacks are done by way of "social engineering", so clearly that term is malleable even within the tech community.
Thanks very much for posting this! I haven't yet finished reading the whole thing, but even just the first section about the history of LLMs, explaining some of the basic concepts, etc., I found to be a very well-written and useful info, and it was really nice that it linked out to source material. So many times when you go into reading stuff about the latest AI technique or feature it can feel like you need to do a…
…was this written by an LLM? I’m starting to doubt anything on the internet that’s overly cherry and polite. Sigh.
B: "Nah, can't be.It's too nice."
I've always thought of the Turing test as an emotional 'feels right' test to find out if something is like us.
The suggestion to use markdown tables was quite interesting. It makes a lot of sense, and I haven't seen it described elsewhere. I have been getting good results by asking GPT to produce semi structured responses based on other aspects of (GitHub) markdown. In general, I find it very helpful to find an already popular format that suits your problem. The model is probably already fluent in rendering that output format…
It can do Mermaid diagrams just fine. Very convenient.
Is it me or is the bot's output in the section "Give a Bot a Fish" incorrect? It states that the most recent receipt is from Mar 5th, 2023 but there are two receipts after that date. This is what worries me about using ChatGPT - the possibility of errors in financial matters, which won't go down well I fear.
I hope all the apps and platforms that rely on ChatGPT have solid legal disclaimers in place as the liabilities could be quite serious.
I wonder if linguistic and English majors would end up benefiting in this trend of "prompt engineering."
Perhaps, but this space suffers from the Armageddon astronaut/fire figher problem. It is easier to teach a Computer Science Major good english, than it is to teach an English Major computer science.
If we cared about English in software engineering as much as we care about code it would be of similar difficulty.
Why are we calling this "engineering"? Isn't engineering the application of science to solve problems? (math, definitive logic, etc.) Maybe one day we'll have instruments that let us reason about the connections between prompts and the exact state of the AI, so that we can understand the mechanics of causation, but until then, I would not think that being good at asking questions is "engineering" Are most 10 year old…