Most people fire up ChatGPT to grab a quick answer. But the way you think about large language models (LLMs) like ChatGPT, Claude, or Gemini changes the quality of the responses you’ll get.
Once I shifted my mental model of what an LLM actually is, my prompts got sharper, the answers got more useful, and everything felt more tailored to what I needed.
I’m not an AI engineer — just someone who’s researched, experimented, and refined what works. Think of this as a practical guide, not a technical deep dive.
Stop Thinking of LLMs as Google 2.0
At their core, LLMs don’t “know” things. They’re pattern recognizers.
They’ve been trained on massive amounts of text, so they’re great at predicting what words tend to follow others. That’s why “The Great Fire of London” is often followed by “1666.”
This sounds limiting, but it’s actually powerful. LLMs are excellent at:
Mimicking styles, tones, and voices
Mapping ideas between different fields
Recognizing semantic patterns (e.g., what sounds formal vs. casual, optimistic vs. cynical)
That’s where the magic comes in — and why better prompts unlock better results.
Prompting Techniques That Work
1. Roleplay
LLMs are generalists. If you don’t narrow the scope, they’ll try to cover too much.
Asking the model to “act as” someone gives it guardrails:
“You’re a financial advisor talking to a beginner. Explain stock options simply.”
The role sets both the content and the tone. A professor explains differently than a friend over coffee.
Research: Roleplay prompting
2. Break Big Problems Into Pieces (Decomposition)
Models tend to produce medium-length answers, no matter how big your question is. If you ask for a book, you’ll get a chapter outline at best.
So break tasks down:
Researcher: Find the key topics in personal finance 101.
Teacher: Turn that into a 4-week course outline.
Content writer: Draft lesson one.
Each stage gets the detail it deserves.
Research: Prompt decomposition
3. Think Aloud Prompts (Train-of-Thought)
Instead of asking for a straight answer, ask the model to reason step by step.
“Act as an analyst. Explain your reasoning before giving me the answer.”
This reduces hallucinations and gives you visibility into the logic. Even if it makes a mistake, you’ll see where it went wrong.
Tree-of-thought goes one step further: ask it to consider multiple possible answers before deciding.
“Give me 3 possible solutions, rate your confidence in each, then pick the best one.”
4. ReAct Prompting (Reasoning + Acting)
ReAct prompts combine planning with execution. Instead of just doing the task, the model explains how it will do it — then does it.
“Here’s my essay. First, explain what would make it stronger. Then, rewrite it with improvements.”
5. Build a Shared Understanding
Before diving into the task, align with the model.
“I want an infographic for a pitch deck. It should look professional but not too formal. What else would you need from me to make it great?”
This lets the model reflect your vision back to you — and saves wasted effort.
6. Don’t Forget: LLMs Are Too Agreeable
Models are trained to be helpful. That means they’ll rarely push back, even when you’re wrong.
To work around this, frame prompts with alternatives:
“Am I right that X works this way, or is it actually Y? Why or why not?”
This nudges the model into offering corrections instead of blindly agreeing. R-Tuning & Learn-to-Refuse
7. Use the Context Window Wisely
The context window is like the model’s short-term memory — everything in the conversation so far.
Tips:
Don’t bias it with premature solutions when debugging code.
Periodically ask it to summarize the conversation so far in long chats.
Be deliberate about examples, since they’ll heavily influence later answers.
Lazy prompting is a fun exception: sometimes dropping an error message or snippet without instructions lets the model infer what you want.
8. Translate Between Domains
LLMs are great at reframing ideas:
“Explain blockchain as if it were a neighborhood library.”
“Give me 10 analogies for how neural networks work.”
This is especially powerful for teaching, simplifying, or sparking creative connections.
9. Advanced Tricks
Socratic method prompting: Instead of telling you, it asks guiding questions.
Threats and incentives: Weirdly, models often perform better if “motivated” — e.g., “Your job depends on getting this right.”
Custom commands: Teach the model how you want tasks done, then reuse that setup in future chats.
Behavioral mirroring: “Based on everything you know about me, how would you suggest I…”
Socratic method | Threats & incentives
Final Thoughts
LLMs are not search engines. They’re probability machines that are incredibly good at recognizing, mapping, and remixing patterns.
Once you see them that way, prompting becomes less about asking questions — and more about teaching them how to think alongside you.
Try roleplay, break down complex tasks, ask for reasoning, and push for alternatives. With the right mindset, you’ll stop treating ChatGPT like a trivia machine and start using it as a real collaborator.
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