I’m experimenting with large system-level instruction blocks for business automation GPTs (director-style agents).
The tricky part is finding the right density of instructions.
When the system prompt is:
• too small → drift, tone inconsistency, weak reasoning
• too large → model becomes rigid, ignores the user, or hallucinates structure
My tests show the sweet spot is around:
– 3–5 core principles (tone, reasoning philosophy, behavior)
– 3–7 structured modes (/content_mode, /analysis_mode, etc.)
– light but persistent “identity kernel”
– no more than ~8–10 KB total
I’d love to hear from people who design multi-role prompts:
• do you rely on a single dense instruction block?
• do you extend with modular prompt-injection?
• how do you balance flexibility vs stability?
Any examples or architectures welcome.