hanhbebong
Newbie
- Dec 29, 2025
- 5
- 3
Hello brothers and sisters on BHW,
Lately, I’ve noticed that many people are frustrated when working with AI. A lot of comments revolve around things like AI being overrated, outputs being generic, or models not being “smart enough” for real work.
As I read those threads, I see a lot of my past self in them. I went through the same phase — blaming the tool instead of looking at how I was using it. After working with AI daily for analysis, content, and operational tasks, one thing became very clear to me: most AI failures come from bad input, not bad models.
Today, I want to share some observations about AI input, purely as a contribution to the BHW community. No selling, no hype — just practical experience.
Preparation: Understand how AI actually works
AI doesn’t understand your business by default.
It doesn’t know:
Step 1: Define the role clearly
If you don’t assign a role, AI responds like an average internet writer.
Instead of:
“Analyze this data”
Try:
“Act as a performance marketer optimizing for CPA and ROI.”
Same data, completely different output quality.
Step 2: Provide real context (not fluff)
Context doesn’t mean long prompts.
It means relevant boundaries:
Step 3: Give a decision-oriented task
Vague tasks lead to vague answers.
Bad tasks:
Step 4: Force a usable format
If you don’t specify output format, AI will ramble.
Once you ask for:
Final thoughts
After spending enough time with AI, I don’t think it replaces skill or experience. What it really does is amplify clarity. If your thinking is messy, AI exposes that very quickly. If your thinking is structured, AI becomes a serious leverage tool.
Some people will agree with this, some won’t. That’s normal. I’m not here to argue or convince anyone — just sharing what consistently worked for me.
Try tightening your input before blaming the model. You’ll notice the difference yourself.
If there’s interest, I can share follow-up posts on:
Peace.
Lately, I’ve noticed that many people are frustrated when working with AI. A lot of comments revolve around things like AI being overrated, outputs being generic, or models not being “smart enough” for real work.
As I read those threads, I see a lot of my past self in them. I went through the same phase — blaming the tool instead of looking at how I was using it. After working with AI daily for analysis, content, and operational tasks, one thing became very clear to me: most AI failures come from bad input, not bad models.
Today, I want to share some observations about AI input, purely as a contribution to the BHW community. No selling, no hype — just practical experience.
AI doesn’t understand your business by default.
It doesn’t know:
- what you’re selling
- who your audience is
- which metrics matter
- what constraints you’re operating under
If you don’t assign a role, AI responds like an average internet writer.
Instead of:
“Analyze this data”
Try:
“Act as a performance marketer optimizing for CPA and ROI.”
Same data, completely different output quality.
Context doesn’t mean long prompts.
It means relevant boundaries:
- price range
- market (US, EU, local)
- traffic source
- business model
Vague tasks lead to vague answers.
Bad tasks:
- “Give suggestions”
- “Improve this”
- “What do you think?”
- “Identify bottlenecks”
- “Decide what to kill”
- “Prioritize next actions”
If you don’t specify output format, AI will ramble.
Once you ask for:
- tables
- checklists
- action plans (kill / fix / scale)
After spending enough time with AI, I don’t think it replaces skill or experience. What it really does is amplify clarity. If your thinking is messy, AI exposes that very quickly. If your thinking is structured, AI becomes a serious leverage tool.
Some people will agree with this, some won’t. That’s normal. I’m not here to argue or convince anyone — just sharing what consistently worked for me.
Try tightening your input before blaming the model. You’ll notice the difference yourself.
If there’s interest, I can share follow-up posts on:
- how I design AI input for automation
- how to use AI for decision-making instead of content fluff
- real examples: same data, different input, totally different output
Peace.
