Write prompts that work
Master the art of crafting clear, effective AI prompts to boost your productivity and communication with advanced tools.
Prompt engineering is no longer that niche skill you hear talked about among developers and AI enthusiasts.
As large language models (LLMs) become more integrated into every industry’s workflow, understanding how to craft and manage an accurate and authentic prompt is quickly becoming a valuable skill for professionals everywhere.
Key takeaways
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Having strong prompt engineering skills makes AI more accessible—no coding required. -
Managing, testing, and optimizing prompts improves performance across language models. -
Learning how to use prompt engineering can empower users to create more accurate, efficient, and personalized outputs from large language models.
Index
Whether you’re building LLM applications, experimenting with image generation, or simply trying to get better results from your favorite AI assistant, the right prompt engineering tools can help you unlock the full potential that these AI models are designed to provide.
We all know that AI is quickly changing the way we work, communicate, and solve problems—but tapping into its true power means you’ll need to know how to do more than just ask questions.
Instead, you need to be able to construct those questions in a way that gives you the best results.
This is where prompt engineering comes in.
What is prompt engineering?
At its core, prompt engineering is half art and half science.
Prompt engineering allows a user to craft effective inputs (or “prompts”) that guide a chosen language model to produce useful, accurate, and contextually relevant outputs.
Here are a few examples to help you understand how this works in everyday scenarios:
1. A good prompt can summarize complex information.
Prompt: “Summarize this article about quantum computing in simple terms for a high school student.“
Why it works:
It sets a clear task (summarization), defines the tone (simple), and specifies the audience (high school student), all of which then guide the model to tailor its response accordingly.
1. A good prompt can summarize complex information.
Prompt: “Write a short story about a robot who learns to paint, in the style of a children’s bedtime tale.“
Why it works:
It combines a subject (robot painting), a format (short story), and a style (bedtime tale), helping the model produce imaginative and age-appropriate content.
3. A good prompt improves professional communication.
Prompt: “Rewrite this email to sound more professional and concise: ‘Hey team, just wanted to say we should probs meet tomorrow to talk about the new work hours.“
Why it works:
It gives a clear goal for the tone (professional and concise), and provides the original text for context, enabling the model to refine for clarity.
4. A good prompt can assist with data analysis.
Prompt: “Explain what this chart shows about monthly sales trends, and suggest two possible reasons for the dip in sales in July.“
Why it works:
It asks for a specific interpretation (explain chart) along with an analysis (suggest reasons), which prompts the model to think critically and provide insights.
These are just a few of the ways in which different types of prompts can provide different types of results.
Learning to shape your prompts with intention—whether through tone, structure, or specificity—is the key to unlocking more consistent, creative, and accurate outputs from any AI model you choose to use.
Top prompting tools to use now
As prompt engineering evolves, so do the techniques and frameworks that help users get more reliable, nuanced, and intelligent outputs from powerful AI models.
These tools are conceptual strategies that help guide how we interact with AI models to solve complex tasks, generate creative content, or perform NLP tasks with precision.
Here are some of the most effective AI prompt generator tools and techniques that you can start using today:
Chain-of-thought prompting
What it is: Chain-of-thought prompting encourages the model to reason step-by-step before arriving at a final answer. Instead of asking for a direct response, you guide the model to walk through its logic—similar to how a human might solve a problem out loud.
Why it works:
This method improves accuracy for tasks that require reasoning, such as math problems, decision-making, or multi-step analysis. It helps the model avoid jumping to conclusions and instead follow a structured path.
Few-shot prompting
What it is: Few-shot prompting involves showing the model a few examples of the desired input-output behavior before asking it to perform a similar task. This technique leverages few-shot learning to help the model generalize from limited data.
Why it works:
It’s especially useful when you want consistent formatting or tone, or when the task is complex and needs context. Few-shot learning helps the model “learn” from a few lines of examples without needing full fine tuning.
Instruction-based prompting
What it is: This technique uses clear, directive language to tell the model exactly what to do. It’s often used in language model programs that are trained to follow instructions closely.
Why it works:
Instruction-based prompts reduce ambiguity and improve reliability. They’re ideal for tasks like formatting, rewriting, or extracting specific information.
Iterative prompting
What it is: This technique involves refining a prompt based on the model’s response. You start with a general prompt, evaluate the output, and adjust the wording or structure to improve results.
Why it works:
It’s a hands-on way to optimize prompts and learn how different phrasing affects the model’s behavior. Great for maintaining prompts over time.
How advanced prompt engineering tools can make a difference
As your prompting skills become more sophisticated, you’ll learn ways to use them not just to get better answers, but also to design entire workflows, automate complex tasks, and collaborate more effectively with AI.
Advanced prompt engineering tools amplify this potential by giving you the structure, speed, and insight you need to use prompt engineering as effectively as possible.
Here are a few ways that you can use your advanced prompt knowledge to get bigger results:
Try out different prompt ideas quickly and see which ones work best: One of the biggest advantages of modern prompt engineering tools is the ability to experiment rapidly. Instead of spending hours crafting one perfect prompt, you can test multiple versions side by side to see which one gives the most accurate, creative, or useful response.
Share and improve prompts with others using tools built by the community: Prompt engineering doesn’t have to be a solo activity. Many open source frameworks and community-built platforms allow users to share their best prompt templates, collaborate on improvements, and learn from each other’s successes and failures.
Use large external knowledge bases so the model can give more accurate and informed answers: For example, if your model is designed to assist medical professionals, integrating it with comprehensive data structures designed for the medical community will allow it to reference peer-reviewed studies, clinical guidelines, and drug interactions–all of which help health professionals do their job with more efficiency and accuracy.
Work with an open-source framework that makes it easier to build, test, and improve prompts without starting from scratch: Open-source frameworks are powerful tools that provide reusable components, prompt templates, and community-driven best practices, making it easier to collaborate with others and improve the development of robust and reliable AI systems.
The result of these techniques? Faster learning, better prompts, and a stronger understanding of how to get the most out of language models.
Master prompt engineering basics
Learn how to write effective prompts that get better results from AI—no experience needed.
Non-techie tips for getting started in prompt engineering
You don’t need to be a programmer or data scientist to become great at writing AI prompts.
In fact, some of the most effective prompt engineers are simply curious thinkers who know how to ask the right questions.
Here’s how you can get started—no technical background required:
Think like a communicator, not a coder
Prompt engineering is more about clear communication than technical jargon.
If you can explain something well to another person, you can probably write a great prompt.
For example:
- Be specific: Instead of “Write a summary,” try “Summarize this article in three bullet points for a busy executive.”
- Use examples: Show the AI what kind of output you want by including a sample.
- Give context: The more background you provide, the better the response.
Experiment freely
Don’t worry about getting it perfect on the first try.
Prompting is a creative process—treat prompt engineering like you’re brainstorming with a very fast assistant.
Try this:
- Write a prompt, run it, and tweak based on the result.
- Change the tone, format, or audience.
- Ask the AI to critique its own response and suggest improvements You’ll learn quickly just by playing around.
Learn from others
There’s a vibrant community of people who are excited to provide human feedback about prompt engineering and are sharing their work online.
Prompt management can come in a variety of forms: you can browse public libraries, join forums, and follow social media accounts that post prompt tips.
Look for:
- Prompt templates for your industry or interests
- Side-by-side prompt versions and comparisons for different prompt styles
- Real-world use cases (e.g., writing emails, analyzing data, generating ideas)
Think in outcomes, not just instructions
Great prompts focus on the result you want, not just the task you’re assigning.
Instead of saying what to do, clarify what success looks like.
Try these strategies:
- Define the goal: Instead of “Translate this,” try “Translate this into French for a 10-year-old audience.”
- Set constraints: Add limits like word count, tone, or format to guide the output.
- Describe the ideal response: Use phrases like “Make it sound professional but friendly” or “Include a call to action at the end.”
Zoom in, then zoom out
Start small, then build complexity. Prompt engineering works best when you layer instructions gradually.
Here’s how to approach it:
Try this:
- Begin with a simple task: Ask for a definition, summary, or list.
- Add layers: Once you get a good base, ask for elaboration, formatting, or transformation.
- Refine with feedback: Tell the AI what worked and what didn’t—then ask it to improve.
Start small, then build complexity. Prompt engineering works best when you layer instructions gradually.
Prompt engineering,
simplified
Take your first step into the world of AI with this beginner-friendly learning path from CodeSignal.
Beyond prompts: Explore more with CodeSignal
Learning to write effective prompts and improve your prompt design is just one part of the AI learning experience.
At CodeSignal, we offer a wide range of courses and challenges tailored for those who want to find ways to use AI in practical, impactful ways.
Here are some ways you can use our platform to help you do this:
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Whether you’re diving into software development or sharpening your prompt engineering skills, CodeSignal equips you with the knowledge and confidence to succeed.
Are you ready to level up your skills, tackle real-world challenges, and unlock your full potential with AI-driven learning? Start at CodeSignal…one skill at a time.
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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