Prompt Engineering: What Actually Works (Without the 8-Hour Hype)

I’ve seen people drop 8-hour-long videos on prompt engineering, and honestly, my reaction is 🤦‍♂️.

I won’t bore you with the obvious stuff or overcomplicate things. Instead, I want to share a few practical techniques that actually helped me write better prompts, some common sense, some hard-earned lessons. Most of what I’m sharing comes from the book Hands-On Large Language Models

So here’s what I’ve learned that actually works:

1. Specificity

This one seems obvious, but it’s also the most commonly missed.

A vague prompt gives you a vague answer. The more precise you are about your goal, format, and constraints, the better the result.

Bad Prompt:

Write something about climate change.

Good Prompt:

Write a 100-word summary on how climate change affects sea levels, using simple language for a high school audience.

See the difference? Specific inputs = Specific outputs.

2. Hallucination Guardrail

We all know that LLMs hallucinate, they confidently make stuff up.

A surprisingly simple trick: Tell it not to.

Try this prompt:

If you don’t know the answer, respond with ‘I don’t know.’ Don’t make anything up.

This becomes really important when you're designing apps or knowledge assistants. It helps reduce the risk of wrong answers.

3. Order Matters

This was a surprise to me and I learned it from the book.

Where you place your instruction in a long prompt matters. Either put it right at the start or at the end. LLMs often forget what’s in the middle (especially in long prompts).

Example:

Here's a paragraph. Also here's a use case. Here's some random info. Now summarize.

Summarize the following paragraph:" [then the content]

Simple shift, big difference.



Other Techniques That Help Me Daily

1. Persona:

Set the role clearly.

You are an expert Python developer who writes clean code.

This changes the behavior completely.

2. Audience Awareness:

My favorite when I want to simplify things.

Explain this like I’m five.

Works brilliantly for breaking down tough concepts.

3. Tone:

Underrated but essential.

Want a formal reply?

Write this in a professional tone for a client. vs Make this sound like I’m texting a friend.

4. Instruction / Context:

Always useful.

Summarize the following news article in bullet points.

Gives the model direction and expected output format.

5. Grammar Fixing:

As a non-native English speaker, this one’s gold for me.

Fix the grammar and make it sound more natural.

It has helped me immensely in writing better content, emails, blogs, even this post :-)

These are the techniques I use regularly. If you have your own prompt engineering hacks, I’d love to hear them, drop them in the comments!



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