Inconsistencies are definitely a challenge with dynamic LLM behavior. For deviations from instructions, I'd recommend refining your prompt engineering to be as explicit and constrained as possible, perhaps even adding negative constraints. For pre-implementation testing, setting up automated end-to-end conversation tests with specific evaluation criteria can help catch these issues reliably before they go live. If you're looking to automate this kind of testing and evaluation at scale, tools like cekura.ai are designed for exactly that.