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AI Prompts That Deliver the Best Results

Updated: Oct 1

AI tools have become integral in generating high quality text, images, and even code. From AI writers to image generators and coding assistants, the key to maximizing their potential lies in crafting precise, thoughtful prompts. We're going to walk through how to structure prompts that yield the best possible results across various AI applications. By understanding the nuances of prompting, you’ll be able to generate more relevant, accurate, and creative outputs.



1. Understanding AI Prompting


At its core, AI prompting refers to the instructions or queries you give to an AI model to guide its responses. Whether you’re using a text generator like GPT-4, an image generator like DALL·E, or a coding assistant like GitHub Copilot, the quality of the prompt heavily influences the output.


AI models don't "think" like humans. They generate outputs based on statistical patterns derived from the vast datasets they were trained on. This means that clarity, specificity, and context in your prompts are critical.


2. General Principles for Crafting Effective Prompts


Before diving into specifics for text, image, and coding AI, there are overarching principles that apply to all AI prompts:


a. Be Specific

The more detail you provide, the better the AI can understand what you're asking for. Vague prompts often lead to irrelevant or shallow responses, while specific prompts yield precise and nuanced results.


  • Example (Text): Instead of asking, "Write about dogs," try "Write a 300-word article discussing the benefits of adopting rescue dogs, with a focus on emotional and physical health."


b. Include Context

Context helps guide the AI’s logic and decision-making. Providing background information or specific instructions will significantly improve the quality of the output.


  • Example (Image): Rather than asking, "Create an image of a landscape," use "Create an image of a snowy mountain landscape at sunrise, with a clear blue sky and a river running through the valley."


c. Set Clear Constraints

Adding parameters, such as word count, formatting, style, or coding language, helps AI understand the scope of your request and avoids irrelevant or off-topic content.


  • Example (Coding): Instead of requesting, "Write a function," specify, "Write a Python function that calculates the factorial of a number using recursion."


d. Iterate and Refine

Sometimes, the first result may not meet expectations. Adjusting prompts based on what the AI delivers can lead to significant improvements. The iterative process is crucial for achieving the highest-quality results.


3. Crafting Text Prompts for Best Results


Text-based AI models like GPT-4 are widely used for tasks like content generation, summarization, and answering questions. To extract the best text-based outputs, the following strategies can be applied:


a. Clarify the Type of Text

Whether you want an article, story, technical documentation, or email response, be explicit about the type of text you expect. The AI will tailor its response based on this.


  • Example: "Write a formal business email explaining a delay in product shipment due to manufacturing issues. Keep the tone professional but empathetic."


b. Define the Audience

Tailoring content to the appropriate audience helps the AI adjust the tone and complexity of the language.


  • Example: "Create a blog post about cloud computing targeted at beginner-level readers with no prior technical knowledge."


c. Specify the Structure

If you need content with a particular format, make sure to state that in the prompt.


  • Example: "Create a 5-paragraph essay discussing the impact of renewable energy, including an introduction, three body paragraphs, and a conclusion."


d. Control Tone and Style

Indicating the desired tone or writing style helps fine-tune the output to your needs.


  • Example: "Write a product description for a smartwatch in a fun, casual tone, highlighting its fitness tracking features and long battery life."


e. Use Constraints

Text length and structure constraints prevent over-delivery of irrelevant content.


  • Example: "Write a 200-word summary of a scientific paper on climate change solutions, with a focus on carbon capture technology."


4. Creating Image Prompts for Best Visual Results

Image generators like DALL·E rely heavily on descriptive language to create accurate visual outputs. These models need to understand not only what you're asking for but also how to depict it visually.


a. Be Visually Descriptive

Use precise adjectives to describe colors, shapes, lighting, and details. The more information you provide about the visual elements, the closer the AI’s output will be to your vision.


  • Example: "Generate an image of a golden retriever sitting in a green park during a sunny day, with trees and a lake in the background."


b. Specify Composition

Detail the arrangement or layout of elements in the image, especially if there are multiple subjects.


  • Example: "Create an image of a cozy coffee shop interior, with a wooden counter, barista making coffee, and a customer sitting by the window reading a book."


c. Include Artistic Style

If you’re looking for a particular art style, mention that in the prompt. Whether it's realism, impressionism, or minimalism, stating the desired style helps refine the result.


  • Example: "Create a minimalist line drawing of a cat sitting on a windowsill, with the sun shining through."


d. Indicate Time and Environment


The time of day, season, and weather conditions can dramatically affect an image’s atmosphere. Including these details can help deliver the mood you're aiming for.


  • Example: "Generate an image of a busy New York City street at night, with neon signs, yellow taxis, and rain-soaked pavement reflecting the lights."


5. Crafting Coding Prompts for Best Results

AI-powered coding assistants like GitHub Copilot or OpenAI’s Codex are revolutionizing the way developers write code. However, providing a well-structured prompt is essential for obtaining useful code.


a. Define the Programming Language

Always start by specifying the language you need, whether it’s Python, JavaScript, or another language. This prevents ambiguity in the code output.


  • Example: "In Python, write a function that sorts a list of integers using the merge sort algorithm."


b. Be Explicit About Functionality

Clarify the specific functionality or algorithm you're looking for, including any edge cases or limitations.


  • Example: "Write a Python function that takes a list of names and returns the list sorted alphabetically, ignoring case sensitivity."


c. Provide Input and Output Specifications

Defining the input and output helps the AI model generate code that fits within your operational requirements.


  • Example: "Write a Python function that takes an integer n and returns a list of all prime numbers less than n."


d. Request Comments for Clarity

If you're looking for educational or understandable code, asking for inline comments can help explain each step of the code’s logic.


  • Example: "In Python, write a function to calculate the Fibonacci sequence up to the 10th number, and include comments explaining each step."


e. Include Error Handling

Many AI models can generate basic functionality, but it’s essential to explicitly request error handling for robustness.


  • Example: "Write a Python function that reads a file and returns the number of lines, including error handling for cases where the file might not exist."


6. Common Mistakes

a. Overloading the Prompt

Giving the AI too much information or conflicting instructions can confuse it and lead to suboptimal results. Keep prompts focused and simple.


b. Being Too Vague

Under-clarified prompts can lead to generic or irrelevant outputs. AI models thrive on detail, so be as specific as possible.


c. Not Iterating

One of the most common mistakes is accepting the first output without refinement. Iteration is key to generating high-quality results, especially with AI tools.


Here's a prompt cheat sheet to reference.



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