How do you approach Context Engineering when building with OpenAI models?

Lately, I have been experimenting with how to feed context into GPT models more effectively.

For example, when fine-tuning or working with larger context windows, I have noticed that the dilemma is in organizing the surrounding information, rather than the prompt itself. Last week, I came to know that it's called Context Engineering.

I am curious how others here approach it:

• How do you decide what information is essential vs noise when building AI workflows?

• Have you noticed cases where too much context actually weakens the model’s output?

Would love to know about strategies or templates that worked for you while engineering the model context.