Most people think prompting is about asking sharper questions.
My experience taught me something different.
LLMs perform at their best when you give them the right context. Not just hints or fragments. Real context that mirrors how a human would understand the situation.
When we use these models every day, we usually skip this part. We jump straight to the question and expect the model to fill in the gaps. That is where hallucinations and weak answers start appearing.
I used to do the same until I noticed a pattern.
Whenever I shared full context first and only then asked my questions, the responses became sharper, more accurate, and more aligned with what I needed.
So I changed my workflow.
My first step is always to prime the model with the background, constraints, and details of the task.
My second step is to ask the actual question.
This simple shift reduced hallucinations, improved quality, and saved a lot of time.
Takeaway
If you want reliable output from LLMs, treat context as the starting point, not an afterthought