Your LLM output sucks because your prompt is shallow
I studied how OpenAI trains these models
Here are 10 deep prompting techniques that get insane results:
I studied how OpenAI trains these models
Here are 10 deep prompting techniques that get insane results:
You’re going to learn:
• What great prompts look like
• How to structure them for better output
• 10+ expert techniques that boost accuracy, logic & creativity
Whether you're a beginner or pro this will level you up.
• What great prompts look like
• How to structure them for better output
• 10+ expert techniques that boost accuracy, logic & creativity
Whether you're a beginner or pro this will level you up.
1. Beginner: Zero-Shot Prompting
Give the model a clear, specific instruction.
✅ "Summarize this article in 3 bullet points."
❌ "What do you think about this?"
Clarity > Creativity at this stage.
Give the model a clear, specific instruction.
✅ "Summarize this article in 3 bullet points."
❌ "What do you think about this?"
Clarity > Creativity at this stage.
2. Beginner: Few-Shot Prompting
Show it examples. Like teaching by demonstration.
Prompt:
Q: What’s 5+5?
A: 10
Q: What’s 9+3?
A: 12
Q: What’s 7+2?
A: ?
This works because LLMs are pattern matchers.
Show it examples. Like teaching by demonstration.
Prompt:
Q: What’s 5+5?
A: 10
Q: What’s 9+3?
A: 12
Q: What’s 7+2?
A: ?
This works because LLMs are pattern matchers.
3. Intermediate: Chain-of-Thought (CoT)
Make the model "think step-by-step."
This boosts reasoning dramatically.
Instead of:
"What's 13 * 17?"
Try:
"Let’s solve this step by step."
It will explain its thinking before answering.
Make the model "think step-by-step."
This boosts reasoning dramatically.
Instead of:
"What's 13 * 17?"
Try:
"Let’s solve this step by step."
It will explain its thinking before answering.
4. Intermediate: Auto-CoT
Don't want to write examples yourself?
Auto-CoT does it for you.
Prompt the model to generate its own demos:
"Here are a few examples. Let’s think step by step."
Now you’ve got scalable reasoning with less effort.
Don't want to write examples yourself?
Auto-CoT does it for you.
Prompt the model to generate its own demos:
"Here are a few examples. Let’s think step by step."
Now you’ve got scalable reasoning with less effort.
5. Intermediate: Self-Consistency
Ask the model the same question multiple times.
Then pick the most common answer.
Why?
Because LLMs can vary and the most repeated answer is often the most reliable.
Ensemble thinking, but faster.
Ask the model the same question multiple times.
Then pick the most common answer.
Why?
Because LLMs can vary and the most repeated answer is often the most reliable.
Ensemble thinking, but faster.
6. Advanced: Tree-of-Thoughts (ToT)
Don’t stop at one reasoning path.
Explore many, like a decision tree.
The model proposes, tests, and chooses from its ideas.
It’s how GPT-4 solves riddles, puzzles, strategy games.
Don’t stop at one reasoning path.
Explore many, like a decision tree.
The model proposes, tests, and chooses from its ideas.
It’s how GPT-4 solves riddles, puzzles, strategy games.
7.Advanced: Graph-of-Thoughts (GoT)
Human thought isn’t linear.
So why force your prompts to be?
GoT lets LLMs combine, backtrack, and remix ideas.
Think of it like brainstorming with memory.
Great for creativity, planning, design.
Human thought isn’t linear.
So why force your prompts to be?
GoT lets LLMs combine, backtrack, and remix ideas.
Think of it like brainstorming with memory.
Great for creativity, planning, design.
8. Advanced: Self-Refine
Prompt → Output → Self-Critique → Improved Output
Let the model fix itself.
Prompt:
"Write a tweet. Now critique it. Now rewrite it based on your feedback."
This loop improves clarity, tone, and logic.
Prompt → Output → Self-Critique → Improved Output
Let the model fix itself.
Prompt:
"Write a tweet. Now critique it. Now rewrite it based on your feedback."
This loop improves clarity, tone, and logic.
9. Expert: Chain-of-Code (CoC)
Want precision? Ask the model to reason in pseudocode or actual code.
Why?
Code forces structure and logic.
It reduces fluff, boosts accuracy.
Example:
"Write code to solve this step by step..."
Want precision? Ask the model to reason in pseudocode or actual code.
Why?
Code forces structure and logic.
It reduces fluff, boosts accuracy.
Example:
"Write code to solve this step by step..."
10. Expert: Logic-of-Thought (LoT)
Inject formal logic.
Prompt the model to identify, verify, and reason using rules like:
If A implies B, and A is true, then B must be true.
Perfect for law, ethics, science, structured thinking.
Inject formal logic.
Prompt the model to identify, verify, and reason using rules like:
If A implies B, and A is true, then B must be true.
Perfect for law, ethics, science, structured thinking.
P.S.
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Master AI and future-proof your career.
Our newsletter, The Shift, delivers breakthroughs, tools, and strategies you won't find anywhere else – 5 days a week.
Subscribe today:
Plus, get access to 2000+ AI Tools and free AI courses when you jointheshiftai.beehiiv.com/subscribe
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