Information density of the prompt is the most important factor in my experience.
And interestingly, LLMs seem particularly bad at writing prompts for other LLMs for this reason (you can guide them to be more dense, just speaking by default).
Conciseness is usually a byproduct of information density though.
> LLMs seem particularly bad at writing prompts for other LLMs for this reason
Claude is terrible at this! Probably for the same reason that its writing style in prose is so annoying and full of claudisms.
Claude use to be leader, too. Their metaprompt was great at the time with opus 3
Lexical-priming->semantic-space-constraint;specialized-lexis+=sharp distributional-signature;∴ tight concept-cluster; generic-lexis->diffuse-activation, broad candidate-set;Attention-heads key/query-match domain-tokens;"Hamiltonian"->{operator,eigenstate,quantum,energy}->register+domain locked;Net:constrained-decoding,vocab=soft-prior over output-distribution; register-matching;#taskdef=decompress->continue
Information density of the interpretability of the intent from the perspective of a human (or human-like).
If the intent is not easy to understand, it's information sparse. Because it takes a lot of CPU (or brainpower) to interpret.
You can run gzip on an English sentence to make it more textually dense, but clearly it is not more information dense in this context.
Chatbot expanded this into something that made sense, but I've no idea if it's what you meant. There's an irony there somewhere.
How do you make these compressed prompts like that