Prompt Engineering Is Changing (Here's What Works Now)
A year ago, everyone was obsessing over finding the perfect prompt. "Add 'think step by step' and watch the magic happen!" That kind of thing.
It still matters, but honestly? The game has changed. Here's what I've learned from actually using these tools day-to-day.
The "Perfect Prompt" Era Is Mostly Over
Modern AI models are pretty good at understanding what you want, even if you explain it casually. The difference between a basic prompt and an "optimized" one matters less than it used to.
What matters more now:
- Giving the AI the right context upfront
- Breaking complex tasks into smaller steps
- Knowing when to use different tools for different jobs
Context Is Everything
Here's a practical example. Say you want the AI to help you write a marketing email.
The old approach: spend 10 minutes crafting the perfect prompt with all the right keywords and instructions.
What works better: just paste in examples of emails your company has sent before, explain who the audience is, and ask the AI to draft something similar.
The context does more work than clever wording.
Chaining Tasks Together
For anything complex, I've stopped trying to get one prompt to do everything. Instead, I break it into steps:
- First prompt: gather and organize information
- Second prompt: analyze the information
- Third prompt: create the final output
Each step uses the output from the previous one. It's more work to set up, but the results are way more reliable.
The Honest Bit
There's no secret sauce. The people who get the best results are just the ones who experiment more and pay attention to what works. Try stuff, see what happens, adjust.
That's really it.