🧠 How do you ensure reliability when LLMs randomly change their output style or logic?

Anyone building serious AI systems knows this pain — same prompt, same model, different result.

Sometimes it’s formatting drift, sometimes it’s reasoning quality suddenly dropping for no clear reason.

So here’s the question for experienced builders:

How do you stabilize LLM performance across runs and updates?

Do you rely on prompt enclosures, structured parsers, few-shot consistency prompts, or custom model fine-tuning?

And how often do you re-validate outputs after OpenAI or Anthropic silently tweak model behavior?

Let’s compare methods that actually work — not theory, but real practices for keeping agent workflows stable.

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