> I'm not much interested in the prompt itself, but I am continually amazed that ChatGPT is able to make sense of prompts like this.
GPT4 (and even 3.5) can do all sorts of magic that makes even the latest Bard look like a joke.
You can use GPT4 to compress inputs down to 1/2 the original number of tokens while retaining 100% of the original meaning. (When the GPT4 generated output is pasted back into GPT4 it's, seemingly, the same as posting the original text).
Defining DSLs in GPT3.5 and 4 is easy, and IMHO better than relying on JSON output (especially before the function calling stuff was introduced).
You can give GPT3.5 and 4 a DSL and it'll suggest improvements to the grammar.
You can tell GPT4 it is an experienced LLM prompt engineer and then ask it to write prompts for GPT4 which you feed into GPT4.
You can tell GPT4 it is an experienced python engineer and that it needs to write a program to parse the output of another prompt which you then paste in.
One time after several hours of fruitless debugging, I pasted a source file into GPT4, told it "there is a concurrency bug here, can you find it for me" and out popped the exact line I had screwed things up on. (Hilariously I had explained how to avoid exactly that same bug a couple years prior to some junior engineers... oops)
I'd pay $100 a month for access to a more powerful programmer centric version of GPT4.
For that matter if OpenAI made an announcement that next week they were gating code generation behind a $100 a month fee, I'd probably just be forced to pay up.
GPT4 is absurdly powerful and I am well aware I'm not even making the best use of it compared to what I see others doing.
Because LLM's are causal autoregressive. The output is non-deterministic and is based on a sampling algorithm. It's why things like 'self-consistency' is a thing. It's possible LLM's are better than or equal to best prompt engineers in producing the best "trace" or "prompt" that will lead to the desired sampling "trajectories".
This leans on the LLM’s latent space to hopefully give you better results. The idea is that it’s able to use those key words to form connections that may have been absent otherwise.
The most essential part of the prompt is the first section(s) that does message numbering, and then the parts that enable you to do operations using message numbers or ranges of them.
After using message numbering in ChatGPT I can never go back to working without that.
For example suppose you have a long chat with a bunch of iterations on some idea or piece of content.
You can simply say “Continue from //16” to continue the chat from that message (basically making a branch) without having to copy and paste it again.
Or you can say “revise //12 but make it more professional and also merge with //distill 5-7” to revise message 12 and merge with the distilled points from messages 5 through 7.
There are many more powerful things you can do with the /t tagging and /s sets, and the //! Loop command and //f function command to automate repetitive tasks such as expanding content, pulling sets of messages for a purpose, or iteratively writing a longer article etc.
GPT4 (and even 3.5) can do all sorts of magic that makes even the latest Bard look like a joke.
You can use GPT4 to compress inputs down to 1/2 the original number of tokens while retaining 100% of the original meaning. (When the GPT4 generated output is pasted back into GPT4 it's, seemingly, the same as posting the original text).
Defining DSLs in GPT3.5 and 4 is easy, and IMHO better than relying on JSON output (especially before the function calling stuff was introduced).
You can give GPT3.5 and 4 a DSL and it'll suggest improvements to the grammar.
You can tell GPT4 it is an experienced LLM prompt engineer and then ask it to write prompts for GPT4 which you feed into GPT4.
You can tell GPT4 it is an experienced python engineer and that it needs to write a program to parse the output of another prompt which you then paste in.
One time after several hours of fruitless debugging, I pasted a source file into GPT4, told it "there is a concurrency bug here, can you find it for me" and out popped the exact line I had screwed things up on. (Hilariously I had explained how to avoid exactly that same bug a couple years prior to some junior engineers... oops)
I'd pay $100 a month for access to a more powerful programmer centric version of GPT4.
For that matter if OpenAI made an announcement that next week they were gating code generation behind a $100 a month fee, I'd probably just be forced to pay up.
GPT4 is absurdly powerful and I am well aware I'm not even making the best use of it compared to what I see others doing.