Prompt Engineering: How to stop your AI prompt from breaking when you change input lengths

When I started building automated content pipelines I kept running into problems. Every time someone pasted an article the AI would mess up. It would. Skip the final instructions. The context window got too full. Everything fell apart.

The key fix was changing how I structured the prompt. Of writing one long block of instructions I started using XML-style tags to separate different parts. I now put the formatting rules at the end—right after the input data. That way the model sees the content first then gets the rules right after. It pays a lot attention when the instructions come after the data.

I rewrote 40 different workflow prompts this way. The error rate dropped from 15% to than 2%. That’s a difference. It shows that where you place your instructions matters more, than how warnings you add.

So what method do you use to keep language models focused on your formatting rules? Do you use tags? Do you put rules at the end?. Do you try something else?