Du bist schlecht im Formulieren von Anfragen. Das ist normal. Hier ist, wie du es schnell verbessern kannst.
In diesem Artikel erfahren Sie, wie Sie Ihre Anfragen an KI-Modelle verbessern koennen, um bessere Ergebnisse zu erzielen. Wir zeigen Ihnen, wie Sie Ihre Anfragen strukturieren und formulieren koennen, um die Genauigkeit der Ergebnisse zu erhohen.
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.
Die ultimative Abkürzung zu einwandfreien KI-Ergebnissen
Verschwende keine Zeit mehr mit Trial-and-Error-Prompts. Erziele konsistente, professionelle KI-Ergebnisse beim ersten Versuch – jedes Mal.
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.
That is why prompting feels random when you are new. It is not random. It is under specified.
If you want the deeper mental model, read: Prompting is not asking. It’s programming with words.
The 3 move fix that improves prompting in 10 minutes
You do not need 50 techniques to get better AI results.
Most wins come from these three moves:
Give the model a role (persona)
Give it context (facts, constraints, inputs)
Specify the output (format, tone, length, rules)
I call it RCO: Role, Context, Output.
Move 1: Role, stop letting the model be “nobody”
When you say “write an apology email,” who is writing it?
Nobody. That is why it sounds generic.
Instead, assign a role that matches the outcome you want.
Example role prompt:
You are a senior site reliability engineer writing to both customers and engineers.
This instantly narrows the voice, vocabulary, and priorities. The model stops producing “corporate fog” and starts producing a point of view.
Move 2: Context, the fastest way to reduce hallucinations
Most hallucinations are missing information.
Whatever you do not include, the model will try to fill in. Not because it is malicious. Because it is designed to complete.
So give it the facts you actually know and be explicit about what it must not invent.
Context checklist you can paste into any prompt:
What happened
Who this is for
What is true, what is unknown
What you want the reader to do next
Constraints: time, tone, length, compliance, brand rules
If you want a full breakdown, read: Context is king: how to reduce hallucinations fast (coming soon).
Move 3: Output, tell it what “good” looks like
Most beginners ask for “a good email.” That is not a spec.
Tell the model exactly how the result should look.
Output requirements that create consistent results:
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 im Cloudflare Stil
Verfasst ein kurzes, faktenbasiertes Incident Update auf Deutsch im Cloudflare SRE Stil, mit Betreff, Bullet Timeline, unter 200 Wörtern, ohne Floskeln. Fehlende Details werden als „Ich weiß es nicht“ markiert und mit den benötigten Infos benannt.
**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.
Zusammenfassung
Bessere Anfragen erzielen bessere Ergebnisse
Strukturieren Sie Ihre Anfragen
Formulieren Sie Ihre Anfragen praezise
Verwenden Sie Kontext und Rollen
Erzielen Sie konsistent bessere Ergebnisse
Du bist schlecht im Formulieren von Anfragen. Das ist normal. Hier ist, wie du es schnell verbessern kannst.
