As generative AI continues to grow as a tool in our everyday lives, prompt engineering has emerged as one of the most critical skills for unlocking the full potential of large language models (LLMs) and how they interpret, respond to, and collaborate with human instructions.

Prompt engineering isn’t just for those who have a degree in computer science or are masters at coding.

Today, it’s quickly becoming a cross-disciplinary skill essential for marketers, educators, analysts, designers, and anyone working with large language models to solve complex tasks.

Key takeaways



  • Prompt engineering is essential for maximizing large language model performance.


  • Clear instructions, examples, and structure lead to more accurate and efficient prompts.


  • Techniques like few-shot and chain-of-thought prompting improve results across real-world applications.

Index

Whether you’re crafting a Python function, generating new blog content, or asking for a step by step tutorial, the ability to write effective prompts is going to dramatically improve your chosen model’s performance and how it responds to your requests.

In 2025, prompt engineering is no longer just about asking questions.

It’s now about designing the types of questions that will guide models toward accurate, relevant, and actionable outputs.

Let’s do a deeper dive into what techniques and strategies are evolving today and how you can best use them to your advantage.

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Precision in instructions: Specific prompts are better prompts

One of the most fundamental prompt engineering techniques being as precise as possible.

The more vague your instructions, the more vague the results.

The best prompts are those that minimize the model’s guesswork by clearly defining the task, context, desired format, and tone.

For example, instead of starting with a prompt like: “Explain climate change,” try instead: “Write a 3-paragraph summary of climate change for high school students, using bullet points and a neutral tone.”

This level of specificity helps the model understand not just what to do, but how to do it. The more specific your prompt, the more likely you’ll get the desired format and content.

Here are some ways to ensure your prompts are as precise as possible:



  • Define the structure: is this for a list, essay, or a table?


  • Specify the audience and tone.


  • Include constraints: what’s the word count, style, audience?


  • Avoid vague instructions that leave room for misinterpretation.

Model behavior: Understand how the model works…and why

To master prompt engineering, you must understand how large language models behave.

These models generate outputs based on patterns in their training data, not real-time reasoning. This means that they don’t “think” in the human sense. Instead, they predict what’s to come based on context, not logic or lived experience.

As a result, a model can produce inaccurate responses if a prompt is unclear or the task is too ambiguous.

In order to be an effective prompt engineer, you’ll need to spend some time experimenting with various models and observing how different prompts influence their behavior.

Know your tools: Few-shot, one-shot, and zero-shot prompting

Prompting is not a “one-size-fits-all” skill. Depending on your question and the outcome you need, there are specific types of prompting that can help guide your model toward a more effective response.

Zero-shot prompts:

You provide the model with a clear instruction, but no examples.

One-shot prompting:

You give the model one example to demonstrate the desired format or behavior.

Few-shot prompting:

You provide multiple examples to establish a clear pattern or behavior.

Think about the following when thinking about your prompting choices:



  • Use multiple examples to teach formatting or style.


  • Ensure examples are diverse but consistent.


  • Place examples before the task to maintain context.

Using examples effectively is a core part of prompt engineering for ChatGPT, helping the model understand desired tone, format, or content style.

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The next level: Chain-of-thought prompting

Chain-of-thought prompting is an advanced technique that encourages the AI model to move through a series of steps before producing a final answer.

This type of prompting is especially useful for math problems, logic puzzles, or multi-step decision-making.

Here’s a good example of how chain-of-thought prompting works:

Think about the following when thinking about your prompting choices:



  • Start your prompt like this: “If a train travels 60 miles per hour for 3 hours, then stops for 30 minutes, and then travels another 90 miles at the same speed, how long did the entire trip take? Please give me the step-by-step answer to this question.”

When you choose to use chain-of-thought prompting, you’re asking the AI model to think out loud—to break down the problem into logical steps before arriving at a final answer.

Fine-tune for more success: Why iteration is key

Even the seemingly best prompts can still use some refinement. Remember that AI models are always evolving and will respond differently depending on phrasing, context, and how complex the task at hand is.

What works once might not work consistently, and small tweaks can lead to dramatically better results.

Here’s how to make sure you’re iterating effectively:

Think about the following when thinking about your prompting choices:



  • Start with a baseline prompt and observe the output.


  • Make small, targeted changes by adjusting the wording, adding examples, or clarifying instructions as you experiment.


  • Compare results and note what improves or degrades the response.


  • Repeat until the model consistently delivers what you need.

Prompt engineering isn’t a one-and-done task—it’s a creative, experimental process.

The more you iterate your prompts, the more you’ll uncover the subtle dynamics that turn a good prompt into a great one.

Prompt engineering,

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Bonus tools for big impact

In 2025, prompt engineers are discovering a whole host of impressive tools that can make a big difference in how their input can affect their output.

Think about the following when writing prompts:

Prompt libraries: Having access to reusable templates for some of your most common prompting tasks can help streamline your workflow and maintain consistency across projects.

Prompt testing platforms: These tools will allow you to compare how different models respond to the same prompt, helping you identify which phrasing yields the best results and where model behavior diverges.

Prompt chaining: Advanced prompting allows you to link multiple prompt components together to guide the model through complex tasks step-by-step. This is especially useful when you write prompts that require you to break a big problem into smaller parts, or for creating outlines before writing full drafts.

These are just a few of the advanced tools that can help you to generate and create prompts that are full of precision, making your AI model’s responses more accurate, authentic, and easy to use.

Move forward with CodeSignal

While the rise of large language models continues to transform how we interact with technology, the real magic happens when we learn to communicate with it effectively.

That’s what makes prompt engineering so powerful.

It’s also why CodeSignal is leading the charge in the best prompt engineering practices for 2025, and beyond.

At CodeSignal, we’ve created practice-based prompt engineering learning paths that empower developers, engineers, and teams to master the art of prompting.

Whether you’re refining your skills or designing complex AI workflows, CodeSignal Learn gives you the platform to experiment, learn, and grow—so you can stay ahead in a world powered by intelligent language.

Reach out and get started with CodeSignal Learn today. Let us help you fine-tune your prompt engineering skills.

If you’re looking to apply these concepts in real-world workflows, exploring prompt engineering for business can give you a competitive edge.

Tigran Sloyan

Author, Co-Founder, CEO @ CodeSignal, Contributor @ Forbes and Fast Company

CodeSignal is how the world discovers and develops the skills that will shape the future. Our skills platform empowers you to go beyond skills gaps with hiring and AI-powered learning tools that help you and your team cultivate the skills needed to level up.

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