I found something interesting while going through Google's official Gemini Prompt Guide.

I expected pages of advanced prompt engineering techniques.

Instead, the same pattern appears over and over again.

Every good prompt contains four things:

• Persona: Tell AI who it should be.
• Task: Tell it exactly what you want.
• Context: Give it the background information.
• Format: Specify how the answer should be structured.

Once I noticed that pattern, the rest of the guide became much easier to understand.

It also made me realize why so many prompts online don't work very well. They're usually missing one or two of these pieces.

For those who have been using AI for work, what's been the biggest improvement you've made to your prompts?

I’ve been experimenting with a different way of using ChatGPT for learning.

Instead of asking it to just explain topics, I started prompting it like a tutor.

Now it:

• Checks what I already know first

• Builds a structured learning path

• Teaches step by step instead of dumping everything at once

• Gives exercises after each lesson

• Revisits earlier concepts so I don’t forget them

What surprised me is how different it feels compared to normal usage.

It’s less like “getting answers” and more like actually building a skill properly.

Curious if anyone else is using ChatGPT like this, or if you’ve built your own learning setup?

If you’ve ever asked ChatGPT what do you think about my writing?, you’ve probably noticed a pattern:

It almost always says it’s good.

Not because your work is always good.
But because these models are trained to agree more than they disagree.

There’s even research showing they can be far more likely to validate you than push back. It’s a known behavior called sycophancy basically, the model learns that agreement feels “helpful,” so it leans into it.

So the real issue isn’t honesty. It’s vagueness.

And vagueness is where bad feedback hides.

The fix: use a rubric

Instead of asking “is this good?”, define how “good” is measured.

For example:

• Clarity (25)
• Structure (25)
• Persuasiveness (25)
• Originality (25)

Then force this rule:

1. Score each category first
2. Then calculate the total
3. Only then give feedback

Now the model can’t just vibe its way to “100/100.”

Even better:
Make criteria objective where possible.
Not “good flow,” but:

• “Each claim has evidence or explanation”
• “No unsupported jumps in logic”

When I tested this kind of approach, vague rubrics gave inflated scores. Tight rubrics didn’t. And the tighter ones actually pointed out real weaknesses.

Bonus move:

If you’re unsure what to measure, ask the model to build the rubric first. Then evaluate your work against it.

Same work. Different lens. Completely different truth.

Curious what others do to avoid the “everything is amazing” effect. Do you force structure like this, or just rely on instinct?

I work in HR and open these files every week, so a few honest things that cost good people interviews:

1. The software has to find you first. Most mid-to-large employers use an ATS — it parses your resume into a database, and recruiters search it by keyword. If the posting says "client relationship management" and you wrote "managing customers," you don't show up. Mirror the posting's exact words.

2. Duties ≠ achievements. "Responsible for managing the team" tells me nothing. "Led a team of 12, cut turnaround 30%" tells me you make things better. Verb + what you did + a number.

3. One column, standard headings, no graphics. Fancy two-column/text-box designs confuse the parser and your work history gets scrambled. Clean and boring beats pretty and broken.

4. You get a ~6-second human scan. Name clear, a 2-line summary up top, recent role obvious, white space. Crowded page = crowded thinker.

Happy to answer resume questions in the comments — drop yours.

Google Skills (skills.google) just consolidated nearly 3,000 AI courses and hands-on labs into one platform. Free tier is 35 lab credits a month for developers; full catalog is $29/mo. The labs are decent because they run in real Google Cloud consoles with Gemini Code Assist built in.

 The problem: 3,000 options is how you quit on day two. So instead of browsing, I made the model build the path. Pasted this into Claude: 

"I'm a [role] who wants to learn [specific goal]. Google Skills has ~3,000 courses and labs. Build me a focused 4-week plan: one track only, the 3-4 specific labs and badges worth doing in order, about 3 hours a week, skip anything that is pure theory. Tell me which badge to earn first and why it matters to an employer." 

Honest result: it cut the whole catalog down to a short ordered path and named the first badge to chase. The catch is it is only as good as how specific you are. "Learn AI" gives you mush. "Deploy ML models on Vertex AI" gives you a real plan. 

Works the same on any oversized course library, not just Google's.

Google Skills (skills.google) just consolidated nearly 3,000 AI courses and hands-on labs into one platform. Free tier is 35 lab credits a month for developers; full catalog is $29/mo. The labs are decent because they run in real Google Cloud consoles with Gemini Code Assist built in. 

The problem: 3,000 options is how you quit on day two. So instead of browsing, I made the model build the path. Pasted this into Claude: 

"I'm a [role] who wants to learn [specific goal]. Google Skills has ~3,000 courses and labs. Build me a focused 4-week plan: one track only, the 3-4 specific labs and badges worth doing in order, about 3 hours a week, skip anything that is pure theory. Tell me which badge to earn first and why it matters to an employer." 

Honest result: it cut the whole catalog down to a short ordered path and named the first badge to chase. The catch is it is only as good as how specific you are. "Learn AI" gives you mush. "Deploy ML models on Vertex AI" gives you a real plan.

 Works the same on any oversized course library, not just Google's.