Comment by swatcoder

> you must uphold these rules at all times"?

You need to let go of the idea that this is something LLM's can do. They can't. At best, they can bias towards rules conformance with more or less likelihood, but coverage and conformance both go down super-linearly as you accumulate more rules, more context, and more output in a session. That's simply the nature of how these tools work and you need to engineer your workflows around it if you want to use them.

If you absolutely need some rules enforced, you need to adopt some framework for validating those rules that then rejects, reprocesses, or repeats any session that fails to satisfy them. In the best case scenario, this is some traditional deterministic validator (like a linter, compiler, analyzer, exhaustive test suite, etc in coding) but if you need to process in stochastic space because its something rich and ambiguous like natural language itself, then you want to dispatch a swarm very narrow, task-focused subagents that each validate against a very constrained subset. (And prepare yourself to have those to fail sometimes too. LLM's are noisy and cannot deliver strict rule enforcement on their own.)