The ultimate guide to AI prompt engineering

You can also use V7 Go to follow our guide below and test different prompt examples.

Are you ready? Let’s start with some core definitions.

What is prompt engineering in AI?

An AI prompt is a carefully crafted instruction given to an AI model to generate a specific output. These outputs can range from text and images to videos or even music.

Prompt engineering means writing precise instructions that guide AI models like ChatGPT to produce specific and useful responses. It involves designing inputs that an AI can easily understand and act upon, ensuring the output is relevant and accurate.

Prompt engineering is essential for improving the performance of AI in various tasks, such as answering customer inquiries, generating content, or analyzing data. Compare two ChatGPT examples below:

If we refine our request, we get a response that aligns more with what we need.

Rather than a rigid technical skill, prompt engineering is more about mastering the art of communicating with AI to achieve consistent, controllable, and repeatable results. Although the term "engineering" suggests a highly technical process, it's actually all about using strategic thinking and problem-solving to interact effectively with AI.

Here is another example of adjusting the results by tweaking the input prompt in Midjourney:

Prompt:

photography, person standing in front of the Eiffel Tower at sunrise, wearing a red coat and holding a cup of coffee --ar 3:2

If we change the first word, we get images that will emulate different styles.

Midjourney images generated for different techniques:

As you can see, some prompts may contain additional parameters or specific commands that are slightly more technical and use specific parameters. For example, the --ar element in Midjourney prompts allows us to use fixed image aspect ratios (--ar 1:1 produces a square).

How to become a prompt engineer?

So, you want to be a prompt engineer? The reality is, that it’s not really a standalone career path.

Let’s use an analogy—

If you think about it, people don’t become writers simply because they’ve just learned to read and write. “Writers” are professional storytellers, journalists, or academics—who just happen to write a lot to tell a story or share knowledge. Writing is a means to achieving specific goals.

And, prompt engineering works similarly. It is more of a skill than a profession of its own.

AI is commonly used by software developers, marketers, lawyers, and teachers. However, in a professional context, prompt engineering usually complements other skills.

To understand how prompt engineering can be applied, let's look at some common use cases.

While prompt engineering has numerous applications, it's also important to understand its limitations and challenges.

Limitations of prompt engineering

When it comes to prompt engineering, there's both good news and bad news.

The good news is that AI has reached a point where it can understand natural language (NLP technologies), allowing us to express our needs in plain, descriptive terms without needing to code.

The “bad” news, however, is that you still need to have a clear understanding of what you want, describe it in detail, and communicate your request effectively.

Moreover, AI can still struggle with nuances, context, and subtleties, meaning it might not always get every aspect of your request perfectly.

Even with very explicit instructions, sometimes you'll still struggle with getting repeatable results. This is especially true when deploying applications and business solutions powered by LLMs. Even with high-performance levels, occasional mistakes are inevitable when processing hundreds and thousands of requests.

That’s why it is important to use tools that help us control the outputs of our models, for instance by limiting responses to a set of fixed options. Here is an example of how V7 Go allows you to determine available options that ChatGPT can use.

Different types of prompt engineering explained.

To get the most out of your prompts, you should understand different types of AI prompts. Here are some common types and techniques:

One-shot and few-shot prompts

These techniques involve providing the AI with examples of the desired task or output before asking it to complete a similar task. By showing the model what is expected through one or a few examples, the AI learns the context and format needed and applies it to new inputs. This is particularly useful for specialized or less common tasks. AI generates a more accurate response based on the example included in the prompt.

Notice that in the conversation above, AI also prepared the lyrics because it tried to mimic the format of the example.

Zero-shot prompts

Unlike few-shot, zero-shot prompts require the AI to perform tasks without any prior examples—based solely on its pre-training. This is used to assess the AI’s ability to generalize from its training to new tasks.

Chain-of-thought prompts

These prompts guide the AI to follow a logical progression or reasoning pathway to reach a conclusion or solve a problem. The prompt encourages the AI to detail its thought process step-by-step, which is helpful for complex decision-making or problem-solving tasks where understanding the rationale is as important as the answer itself. In some cases, even adding a phrase like “explain your reasoning” will improve the quality of our final response.

Iterative refinement prompts

In this approach, the initial response from the AI is refined through subsequent prompts, each aimed at improving or specifying the response further. This can involve correcting errors, asking for more details, or re-directing the AI's approach.

Hybrid prompts

Combining multiple techniques, hybrid prompts might integrate direct instructions with creative challenges or conditional elements with exploratory questions to guide the AI more effectively according to complex needs.

How to learn AI prompt engineering: Start by using an AI prompting tool like V7 Go to compare responses from different models to the same request. Practice few-shot and zero-shot learning techniques. Try out various prompt engineering methods, like using the output of one model as input to another model.

Meta-prompts

These are higher-level prompts that ask the AI to consider its own capabilities or reflect on the type of reasoning it uses. This can be used to adjust its approach or to develop new strategies for answering questions or solving problems.

Each of the prompt engineering techniques mentioned above can be adapted and combined depending on the specific requirements of the task at hand and the capabilities of the AI model being used. Effective prompt engineering involves a deep understanding of these techniques and the ability to apply them creatively.

How to write better AI prompts: prompt engineering guide

To craft effective AI prompts, it's essential to understand the nuances and techniques that can enhance the accuracy and relevance of AI responses. Here are some key aspects to consider:

1. Know the difference between system prompts/main instructions and individual requests

Tools like ChatGPT or DALL-E pair user requests with “system prompts” which provide broader rules. For example, if we request a caricature of Donald Trump or Elon Musk it won’t work because the default system prompt includes a hidden policy, with point 6 stating:

Do not create images of politicians or other public figures. Recommend other ideas instead.

Still, sometimes AI can be tricked into providing results it is not supposed to share. These are known as jailbreak prompts. While fun, it's best to solve specific problems through normal prompt engineering without tricking AI models.

In some cases, we can adjust the core instructions instead. For example, transforming a “helpful assistant” into an “unhelpful assistant.”

When writing your requests, investigate the main prompt the model is using and ensure it doesn’t clash with your specific requests. Sometimes system prompts take precedence over user prompts, and the results may disappoint you. For example, we tell AI to write a 1500-word essay while the system prompt tells AI to keep the answer shorter than 500 words.

2. Provide context and ask for the solution to your ultimate problem

This may sound counterintuitive, but it's very easy to overthink your problem. When AI doesn’t understand your final goal, you can spend hours framing the problem incorrectly. It's better to explain the final goal clearly rather than trying to solve intermediate steps without context.

Here is an example of a person trying to figure out what is the standard 4:3 resolution used in old-school video games. They remember that the height is above 700 pixels.

The answer is quite straightforward.

OK. So, the XGA resolution is 1024x768 pixels.

Now, what would have happened if instead the problem was presented as more of an abstract math puzzle?

We get some more weird calculations and suggestions focusing on mathematical criteria specified in the prompt. But there is no answer to our problem.

Let’s confront ChatGPT with the situation:

3. Get a good grasp of the medium or form you are trying emulate

To write good prompts, we must understand terminology related to styles, forms, and formats. For example, for AI-generated music, it is crucial to have knowledge about different music genres.

Here is what the prompting interface of Udio looks like (notice the Lyrics Tip with hints for effective prompting):

And, here is an example of an AI-generated song. The main prompt uses genres (like Nu-metal) but also moods and other keywords.

Also, the prompt for lyrics contains additional information about the song structure. By adjusting where to put a verse, an instrumental solo, a bridge, a chorus, and so on, we can have better control over the final result.

4. Include one-shot or few-shot examples in the prompt

Providing examples within the prompt helps the AI understand the desired format and style. For instance, if you're asking for a product description for your website, include a few examples of existing descriptions to guide the AI.

With image generators you can also add reference images to influence the results. For example, Midjourney allows you to add image links to your prompt and uses them for guidance.