Simple Prompting Tips: Less Magic, More Method

Overview

From building AI products that serve thousands of users, I've learned prompt engineering isn't about magic words - it's about clear thinking and communication. Here's what actually works.

• The Three Core Principles
• The Practical Toolkit
• Common Pitfalls
• Looking Forward

The Three Core Principles

Let's start with a truth that might ruffle some feathers: most prompt engineering advice is unnecessarily complex. After testing thousands of prompts in production, I've found it boils down to three principles.

1. Context Is Everything

Think about training a new team member. You wouldn't just say "write me a blog post" - you'd explain the company, audience, tone, and what success looks like. Same goes for AI.

Good context includes:

• The role you want the AI to play
• Who the output is for
• What you're trying to achieve
• Any constraints or requirements
• Relevant background information

Example: `❌ "Write a marketing email"

✅ "Write a marketing email for our AI software product. The audience is enterprise CTOs. We want to highlight our new security features. Tone should be professional but not stuffy. Length: 200-300 words."`

2. Structure Breeds Clarity

Want better outputs? Structure your inputs. Here's a pattern that works consistently:

Task: [What you want done]
Context: [Relevant background]
Format: [How you want it structured]
Constraints: [Any limitations or requirements]
Additional Instructions: [Special considerations]

3. Iteration Is Your Friend

Your first prompt will probably be rubbish. That's fine. Great prompting is iterative.

Start simple, see what you get, then refine. The key is being specific about what's not working:

❌ "Make it more formal"
✅ "Rewrite this for a senior executive audience using business terminology"

❌ "This isn't quite right"
✅ "The tone is too casual for a technical whitepaper. Please rewrite using more precise technical language"

The Practical Toolkit

Temperature Control

Temperature controls how "creative" the AI gets. Here's a practical guide: Temperature is a parameter that controls randomness in the model's output, typically ranging from 0.0 to 1.0:

• 0.0: Deterministic, focused responses
• 0.2-0.4: Balanced creativity and consistency (good for business writing)
• 0.7-0.9: More creative, varied outputs (good for brainstorming)
• 1.0: Maximum randomness and creativity

I have also tried temperatures of 2 on the Open AI playground, with unusual results.

Technically, temperature works by modifying how the model samples from its probability distribution of next tokens. Here's what happens under the hood:

1. The model generates a probability distribution for the next token
2. These probabilities are modified using the temperature parameter:
   • Lower temperature "sharpens" the distribution, making high-probability tokens more likely
   • Higher temperature "flattens" the distribution, giving lower-probability tokens more chance

Chain of Thought

Want better reasoning? Ask the AI to show its work. Instead of jumping to conclusions, request step-by-step thinking:

Here, the AI will map it out step-by-step. This is great to ensure the AI is making the right decisions, and that you are following the logic, but it's not always appropriate. For example, if you are generating something that will be shared with an audience, you don't want the step-by-step thinking in there.

It's great for manually iterating the output, but to automate this you would need some fancier techniques in conjunction, for example:

1. Asking the thoughts to be put in <thinking/> tags, then filtering these out programmatically
2. Multi-shot prompting, where you simulate your iteration by passing the initial output back to the AI.

The Power of Examples

Nothing beats showing exactly what you want. Here's a type of template that works:

Format the output like this:
Title: [Example title]
Summary: [Example summary]
Key Points:

- [Example point 1]
- [Example point 2]

Now, using that exact format, write about [your topic]

This is common when you want the model to output JSON. Before Open AI's guaranteed JSON through structured outputs, I found a great way to ensure you get a valid response was with something like:

Format the output like this:

{
"title": "[Example title]",
"summary": "[Example summary]",
"keyPoints": [
"[Example point 1]",
"[Example point 2]"
]
}

Your output should start with { "title":

This increases the probability of the token choosing { as the first token, which is required for valid JSON.

Common Pitfalls

1. Being too vague: "Make it better" is not a prompt
2. Overcomplicating: You don't need fancy techniques for simple tasks
3. Undercomplicating: Yes, now I'm overcomplicating it, but like anything there's a spectrum.
4. Forgetting the audience: Always specify who the output is for
5. Not setting constraints: Without boundaries, you'll get meandering responses. Be clear about your expected output. Structure helps here.

Looking Forward

Traditional prompt engineering will become less important as models get better at understanding natural language. But the principles - clear communication, structured thinking, and iterative refinement - will become more crucial than ever.

The simplest mental model for working with AI is working with people - people that know absolutely nothing about you, the problem, or the context. You need to provide all the necessary info to get good results, else the model will make up its own mind.

The future isn't about crafting perfect prompts - it's about having better conversations with AI. Focus on clarity and structure, and the rest will follow.