You Suck at Prompting. That’s Normal. Here’s How to Fix It Fast.
You Suck at Prompting. That’s Normal. Here’s How to Fix It Fast.
In this article, you will learn how to improve your prompts to AI models to get better results. We will show you how to structure and formulate your prompts to increase the accuracy of the results.
You Suck at Prompting. That’s Normal. Here’s How to Fix It Fast.
If you have ever asked ChatGPT for something simple and got absolute garbage back, welcome to the club.
Sometimes the output is great.
Sometimes it is so wrong you start negotiating with your screen.
And the frustration usually leads to one of two conclusions:
AI is dumb, I’m done with this
I’m dumb, I don’t know how to use this
Most people land on the second one. I did too.
Here’s the good news: prompting is a skill, not a personality trait. And you can improve it fast with a small set of moves that fix most “bad ChatGPT answers.”
Why your results swing from great to garbage
The core issue is simple:
You think you are asking a question.
The model is completing a pattern.
LLMs are prediction engines. They generate the most likely continuation based on what you gave them. If your input is vague, the model has to guess what you meant. If your input is focused, the model “guesses” in a much narrower lane.
The ultimate shortcut to flawless AI results
Stop wasting time guessing prompts. Get consistent, professional AI results right from the first try, every time.
If a detail is not in the context, say “I don’t know”
That last line is huge. You are giving the model permission to not guess.
A quick before and after you can copy
Bad prompt:
Write a Cloudflare apology email.
Better prompt (same task, wildly better output):
Incident Update in Cloudflare style
Write a short, fact-based incident update in German in Cloudflare SRE style, with subject, bullet timeline, under 200 words, without fluff. Missing details will be marked as “I don’t know” and named with the required information.
**Role**
- You are a senior site reliability engineer at Cloudflare writing to customers and engineers.
**Context**
- Incident summary: incident_summary
- Start time: start_time
- End time: end_time
- Impact: impact
- Root cause (facts only): root_cause
- Immediate actions: immediate_actions
- Next steps (confirmed only): next_steps
- Unknowns: unknowns
**Output**
- Subject line included
- Under 200 words
- Timeline in bullet points
- No corporate fluff
- If a detail is missing, say “I don’t know” and list what you would need
That is it. That single structure will improve prompting for beginners more than most “prompt hacks.”
The meta skill that makes every technique work
All of this is really one thing:
Clarity.
Role forces clarity about who is speaking.
Context forces clarity about what is true.
Output requirements force clarity about what “good” means.
When the model output is messy, it often mirrors messy inputs. Think first, then prompt.
What to do once you get a great prompt
Here is the part most people miss:
When you finally land a prompt that works, save it.
Do not let it die in a chat thread. That prompt is now an asset. Reusing it is how you get consistent results without rethinking everything every time.
That leads naturally to the next question: when is it worth moving from “saved notes” to a real prompt library? This post answers it: When a prompt library becomes worth it.
Next step
If you are already reusing prompts weekly, treat your best ones like reusable templates: keep a fixed core and only swap the inputs. If you want a lightweight way to do that without copy paste and without prompt drift, PromptaCore is built around exactly that workflow.
FAQ
Why does ChatGPT give bad answers?
Usually because the prompt is under specified. The model fills gaps with guesses.
How do I improve prompting quickly?
Use Role, Context, Output. Add “If it’s not in the context, say I don’t know.”
What is a prompt template?
A reusable prompt with a fixed core (rules, format, tone) and variable inputs (context fields).
How do I get better AI results consistently?
Stop writing prompts from scratch. Reuse proven templates and standardize the output requirements.
