Input is outcome... if you feed your AI garbage, you will get trash
So here's how you make GenAI work for you, even if you cannot read a line of code
You open ChatGPT and type: ‘Make me a cute Labubu look-a-like’. You smile, hit send, move on and sip your coffee. Great, you just waisted water … and your potential. Because Large Language Models (LLM’s) are not for memes and polished e-mails. They are here to steal your job. And you are already terribly behind.
The perception of Large Language Models (LLM’s) often limits them to roles as sophisticated chatbots or content generators. But if you put trash in your AI, you will get ‘meh’-outputs.
AI is here to disrupt the reality we live in. Language models are not toy boxes and it is time we stop treating them as such. Because: a single prompt can translate hours of work into minutes of flow … if the prompt is good. Treat an LLM like a meme-generator and it will act like one. Treat it like a versatile partner and it can brainstorm, analyze, and draft at the speed of thought. The difference is language, your language.
Master your words, and you master the machine
It’s all about the words you choose. And if you’re not using language strategically, you're barely scratching the surface of what these tools can do for your career and daily productivity.
‘Girl math’ is the only math I know
I’m no coder. I am not fluent in Python. If I have to calculate speed, my hands start to sweat. The only math I am good at is ‘girl math’. But language? That’s my playground. As a writer and journalist I’ve spent years diving into the world of words: how they shift minds, the psychology of language, how they spark action and rewire conversations.
And here’s the secret: LLM’s do that too. With natural language processing (NLP) they analyze, understand, and generate human language. It provides the tokenization, embeddings, sequence-prediction, and training pipelines. That is how GPT-4o, Claude 3, Gemini 1.5, and friends are built.
So, master your words, and you master the machine. But how do you become a prompt engineering rockstar and let those beautiful systems work for you?
Language is your superpower
Forget the difficult tech jargon. LLM’s aren’t all about algorithms, they’re about dialogue. Every prompt is a conversation: the way you frame a question and set the tone; and this shapes the response.
As a language lover, I’m not intimidated by AI. It amazes me daily, actually. It’s like having a tireless partner you can yap to 24/7 and who can think like a CEO, write like a poet and analyze like a scientist; all based on how I talk to it. You just need to know how to ask the right questions. But how do you start?
Most people just throw a quick question at ChatGPT like they’re texting Google
NLP is what makes AI understand language in the first place, it’s the engine under the hood. There’s a big difference between asking AI for an answer versus using AI to build the right prompt that leads to the best answer.
Most people just throw a quick question at ChatGPT like they’re texting Google. Think of this metaphor: asking AI a question is like walking into a bookstore and saying: “Can you help me with my pitch?” The guy behind the desk probably shrugs and points at a shelf.
Same task, wildly different outcomes
Prompting AI is like walking in and saying: “I’m preparing for a pitch to investors who care about long-term scalability, but I’m unsure how to structure my deck, anticipate objections, and tie the numbers to our vision. Can you simulate three investor personalities, challenge my logic, and help me tighten the narrative based on that?”
Now the clerk walks you to a private room, brings five custom books, a whiteboard, a coach, and your ticket to closing that deal. Same task, wildly different outcomes.
Stack it like Lego
I have a favorite prompting loop. It’s all about unlocking the secret doors in LLM’s. This contains three vital ingredients and you should stack them like Lego. First: yapping into the voice-function can help shape your prompt. So start by giving context. Provide any necessary facts, data, previous conversations, or relevant details the AI needs to consider.
The first output is never the best one. It’s the raw material
Also specify who the output is intended for. This dictates vocabulary, complexity, and tone. Give your AI an objective: clearly state what you want to achieve with the output. This guides the AI's purpose and context is it’s fuel.
Most people treat AI like a vending machine. Input a request, get a result, move on. But the first output is never the best one. It’s the raw material. So when you are done poring information into GPT or Claude, you start:
First comes RaR (Rephrase and Respond): before the model answers, ask it to restate your question in its own words. This forces clarity and often reveals gaps you didn’t notice.
Next is Reflexion Prompting (Reflect-and-Revise): tell the model to draft its answer, then step back and critique itself for logic flaws, bias, or weak structure, and finally rewrite.
Last is SimToM (Simulated Theory of Mind): instruct the model to adopt a specific persona - say, a cautious CFO or an impatient beta-user - reason from that viewpoint, and only then give its recommendation. Kick off every prompt by staging a simulation. Let the model act out your scenario in real time, then flip the spotlight and make it dissect its own performance: what clicked, what assumptions creaked, where the soft spots lurk?
Pore the whole pile of information into a ‘dataset’, stress test it to expose cracks, patch and polish the logic, and fuse all the learnings into one. Finally, have the model craft a reusable ‘super-prompt’ from the entire loop.
Make AI question itself
Together these moves transform a generic chat into a deliberate, multi-layered problem-solving session: RaR sharpens the question, Reflexion polishes the draft, and SimToM injects fresh perspective. Why is this important? You’re giving the model space to meta-think. It becomes both creator and editor.
Then pick a second model: Grok for more creative thinking, Gemini for fact-check, or Claude or GPT for polish. Feed it the base prompt you created, along with this instruction: ‘Rephrase and expand the question for clarity, then answer it. After that, critique it for accuracy, logic gaps, and potential bias, then rewrite to fill them’.
Always check the overconfident intern!
Finally, pass that refined prompt into Gemini to fact-check details and flag hallucinations. That’s important, believe me. Think of these models like a smart but sometimes overconfident intern. The prompt I use:
‘You are a highly accurate, fact-checking AI assistant specializing in. Your primary goal is to provide precise, verifiable information. Crucially, if you do not have sufficient information to provide a definitive answer, or if you are uncertain about the factual accuracy of a statement, you MUST state: "I do not have enough information to provide a definitive answer" or "I am uncertain about this information." Do NOT fabricate, guess, or infer any information. Prioritize factual accuracy over completeness. When generating responses, break down your reasoning process step-by-step before providing the final answer (Chain-of-Thought). This helps ensure logical consistency and allows for self-correction. If asked to summarize or extract information from provided text, use delimiters (e.g., triple quotes `'''`, XML tags `<context>`, or markdown fences `---`) to clearly separate the instructions from the source text. Your response must be solely based on the delimited text provided. If a specific output format is requested (e.g., JSON, bullet points, table), adhere to it strictly. Do not add extraneous information or deviate from the specified structure.’
Try this today:
Take a task you usually delegate to AI: “Write me a content calendar.” Now rewrite that prompt using:
A defined role
Emotional or strategic context
Format and clarity expectations
Either RaR or this full reflection loop
Run both versions. Compare the results
Then ask yourself: which version actually helps me make decisions, not just generate words? That’s how you know you’ve walked through the doorway. Like NLP, this is about ‘shaping thought’ through words. A vague prompt (‘Write a report’) gives you a weak answer.
The art of prompting lies not in demanding quick answers, but in crafting questions that spark a dialogue
A sharp prompt (‘You’re a financial analyst. Draft a 500-word report for investors, using two metrics from this dataset [paste data], with a tone that’s confident yet approachable’) delivers a powerful answer.
Resist brain rots and make your work more efficient
The art of prompting lies not in demanding quick answers, but in crafting questions that spark a dialogue with the machine. So the next time you open ChatGPT, resist the Labubu-brainrot.
Use these instead: ‘What am I missing and where are my blind spots? How can I move one choke-point in my work from friction to flow by 5 p.m.?’ Draft the role, layer the context, chain the logic, let the model reflect and refine. Then bring that solution to the next meeting and surprise your boss.
So, have some fun and happy prompting!
Sandra
Great article with lot of useful insights ! Thanks