My colleague typed “write me a product description” into ChatGPT last week. Got back three paragraphs of corporate word soup. His fix? He typed “make it better.” More soup. Then “make it sound less robotic.” Still soup, just warmer. He burned 20 minutes going in circles on a 2-minute task, and eventually just copy-pasted the first version because he ran out of patience.
Sound familiar? Google thinks they know exactly why.
🎯 Why your prompts keep getting mediocre results
Google Cloud published a white paper on prompt engineering, and someone called Sovorel broke it down on Reddit in a way that’s actually readable. The core diagnosis: most people prompt like they’re sending a text. Short, vague, hoping the AI reads their mind.
The thing is, a well-trained AI is basically a very capable new hire. Smart, willing to work, but completely clueless about your context, your goals, and what “good” looks like for you. If you handed a new employee a sticky note that said “write something about the product,” you wouldn’t be shocked when they came back with something unusable. You’d write a proper brief. Structure fixes that. A solid prompt isn’t a question. It’s a brief. And the more specific that brief, the less time you spend on revisions that go nowhere.
🧱 The five markers that get you 1-shot results
Google’s paper identifies five components. Hit all five and you stop burning rounds of back-and-forth:
- The Task: Be specific. “Write an essay” is basically a coin toss. “Write a 500-word analysis of the economic impact of the US Civil War, written for a high school audience” gives the AI something to actually aim at. The more precise you get about format, length, and audience, the less interpreting it has to do on its own. Vague in, vague out.
- Instructions: Give it rules of the road. “Ask me one clarifying question before moving on.” “Use bullet points.” “Keep it under 200 words.” “Don’t use the word ‘leverage.'” Whatever applies, say it out loud. Don’t assume. Instructions are also where you can constrain tone: “write like you’re explaining this to a tired founder, not a textbook reader.”
- Context / Persona: Tell it who to be. “Assume the role of a hiring manager at a university” doesn’t just flavor the response, it anchors the model’s logic to a specific point of view. You can also flip this and tell it who YOU are: “I’m a freelance designer pitching to enterprise clients.” Both angles matter. This one makes a bigger difference than most people expect.
- The “Why” (Reasons): Explain the purpose behind the request. If the AI knows you’re practicing for a real job interview, it makes feedback more critical and direct. If it knows you’re writing for beginners, it simplifies the language and skips the jargon. If it knows this is a sales email, it’ll push toward a clear CTA instead of a neutral summary. Context shapes everything downstream.
- Clarification Prompt: End every prompt with “Do you need any more information from me first?” One small habit that stops the AI from guessing and filling gaps with generic nonsense. More often than you’d think, it’ll surface a question that saves you an entire revision cycle.
🔁 Two techniques worth stealing
Step-Back Prompting: Before asking your specific question, prompt the AI to consider the broader topic first. Something like “What are the key principles behind negotiation psychology?” and then follow up with your actual request: “Now help me write a counter-offer email for a salary negotiation.” It activates deeper background knowledge and reduces shallow pattern-matching. Think of it as warming up the context before the real ask, the way you’d give a consultant five minutes of background before jumping into the problem.
APE (Automatic Prompt Engineering): Use the AI to build your prompt for you. Describe your goal, ask it to write the structural formula, then run that formula. The community verdict: it saves time upfront but you’ll still edit the output. The real win is consistency, not perfection. It’s especially useful if you run the same type of task repeatedly and want a reliable starting template instead of improvising every time.
One more note: “let’s think step by step” used to be essential. Modern models have reasoning built in now, so it’s less necessary across the board. Still worth adding for very complex logic or multi-step math, but you don’t need to paste it into every single prompt anymore.
🤖 The habit matters more than the tool
Manually running through all five markers every time you open a chat gets old fast. A few practical options: keep a prompt template you reuse and paste at the start of new conversations, set a system prompt that pre-loads your context and persona so you don’t repeat yourself every session, or try one of the prompt optimizer tools that inject this structure automatically before the AI sees your request. Some people keep a simple text file called “my prompt kit” with three or four pre-built briefs for their most common tasks. Takes 20 minutes to set up once, saves hours over a month.
The real shift here is realizing that prompting isn’t asking. It’s briefing. Once that clicks, the results follow. And once you’ve got a template that works, you stop dreading the blank chat window entirely.
Have you tried the APE method yet, using AI to write your own prompts? Worth experimenting with if you haven’t.
Frequently Asked Questions
Q: Does APE (Automatic Prompt Engineering) actually save time if I end up editing the generated structure anyway?
Yes, but for consistency over perfection. The real win is building a reusable template that you refine once and apply across many prompts, you’re not optimizing every single chat. As one commenter noted, you can take the APE output and push it into downstream tools like reports or slides rather than stopping at raw text, which is where the actual value shows up.
Q: Do I still need “let’s think step by step” with modern models?
Not as a must-have anymore. Modern AI models have reasoning built in and often do it automatically. That said, explicitly requesting step-by-step reasoning still helps with ultra-complex logic or when you want visible reasoning you can verify yourself.
Q: What’s the quickest way to get started without spending hours architecting perfect prompts?
Start with the five markers (Task, Instructions, Context/Persona, Reasons, Clarification) and test them manually a few times on your specific use case. Once you have a working formula, use APE or a prompt optimization extension to auto-generate the structure for similar tasks, this shifts the overhead from every chat to occasional template creation.
Q: How much more effort is the structured approach really worth?
The structure pays off when you need consistency, you hit 1-shot results instead of iterating back and forth. If you’re only using AI casually, the overhead might not be worth it. But if you’re running prompts regularly for similar tasks, architecting the structure upfront saves time and frustration in the long run.
Sovorel’s breakdown of the Google Cloud white paper on Prompting
by u/Distinct_Track_5495 in PromptEngineering