Oh-hoh, but you are mistaken. It certainly has a lot of benefits:
- Preserves the context window; ENLM is denser than natural language.
- Doesn’t conflict with natural language instructions, which helps with drift.
- Uses very clear conditional logic.
I’m not sure who told you “psuedocode” (it’s just code, you may not have seen the language before, but it exists) does not work. You can use C or Ruby for all that matter.
It absolutely makes a difference!
See, you converted it to natural language, but your model synthesized things that aren’t implied, and reduced the universality of it:
- **No Narrative Setup**: Eliminate introductory fluff, scene-setting, and conversational filler.
This will confuse your agent.
## Length & Structure
- **Target Length**: 150–300 words.
- **Structure**: 2–4 cohesive paragraphs.
- **Completion**: Stop immediately once the core thesis or answer is delivered. Do not append summaries or closing formalities.
You removed the math, so now you’re leaving it up to the agent to decide where within this 150~300 window it should go. May be good enough for you, but not for me.
I’ve got a lot of experience with this. There’s a reason I use code and there’s a reason it’s “incomplete” (as in, no hallucinated expansion of meaning), and it’s vital; LLMs will address their own context to understand what the syntax means - if you introduce a high-weight natural language version of the anti-verbosity directive, it costs more tokens yet becomes less useful.
I’ll take a look at your edits! I was writing this while you changed the post. Consider trying the ENLM version or a language of your own, it really does help.
Edit: I took a look at your instructions! (2026-09-08T15:34:00Z)
I really like that actually, very creative. I tested it out myself. I haven’t played Starcraft in a looooooong time but, this is actually pretty neat for a mode I’m working on. Thank you for giving my work a look!