Mastery of the art of prompt engineering requires skill and finesse. It’s a science that involves uncovering inputs that generate desirable or useful outcomes. With AI technology evolving rapidly, learning how to become a prompt engineer has never been more important.
Stemming from an emergent new property of large language models, prompt engineering is quickly becoming a critical way to have machines execute commands. This article will outline 3 easy steps you can take to become a prompt engineer and level up your skills in this captivating field.
By acquiring these key competencies, you’ll be well on your way to becoming a prompt engineer!
But first… What is an AI Prompt?
AI prompts are instructions to a generative AI model on what output should be produced. Textual interfaces enable users to enter commands that the AI must interpret and follow. Prompts range from simple queries like “Who is the Manager of Manchester United?” to complex problems with multiple facts, and open-ended requests such as “Tell me a story”. Language models such as GPT-3 and ChatGPT respond to these prompts with natural language, while DALL-E and Stable Diffusion rely on describing an intended outcome in detail for image generation purposes. AI prompts can also include files such as CSV data for the model to process.
And What is Prompt Engineering?
Prompt engineering is the process of designing and crafting AI data inputs to train models to perform specific tasks. This includes selecting the right input info, formatting it so that the model can understand and use the data to learn. Properly developed training data will provide AI models with accurate predictions and decisions.
The language models GPT-3 and J1-Jurrasic were huge developments in AI prompt engineering, providing excellent results on novel tasks when employed with multiple NLP datasets. A language model provides a better representation of reasoning when presented with thought progression prompts such as “Let’s think about this step by step”, thanks to zero-shot learning. Machine learning models DALL-E, Stable Diffusion, and Midjourney have been made available for public use for text-to-image prompting capabilities. Despite usage becoming widespread, some users still find themselves struggling in achieving their desired outcomes – this is where becoming competent at prompt engineering is vitally important.
Step 1: Understand the Basics
Prompt engineering is one of the key components of successful Artificial Intelligence projects. It involves carefully refining and constructing prompts in order to produce more accurate and meaningful outputs from AI systems. Writing an effective prompt can be daunting, but with a few simple guidelines, you can craft a prompt that yields higher-quality results.
To start with, it’s important to provide instructions which are clear and concise. Make sure all the necessary elements are included: instructions, questions, input data or examples. Separate each element with ### or “”” so they’re easy to access and understand by the AI model. Additionally, when providing context for the prompt avoid using unnecessary words – say what you want as clearly and concisely as possible. Don’t leave any room for ambiguity.
The next step for AI is to provide enough information about the situation to achieve the desired result, such as structure, style, length, etc. For example, if your goal is to write an essay about Artificial Intelligence – leveraging specific parameters such as key topics, length (e.g., 5 paragraphs, 180 characters), format (e.g., formal, casual) and target audience can help keep the focus on achieving the end result that was expected.
Finally, when working on text-to-image AI tools, it could be useful to upload sample images so that AI models have resources to refer to when processing inputs into related visuals and have a better understanding of what is expected.
Step 2: Practise with ChatGPT
Working with ChatGPT can get you amazing results as it has the potential to generate meaningful conversations and offer insights beyond your wildest dreams. The prompt engineering skills you are learning will make you one of the few people who can make the most of this incredible tool.
Prompt engineering with ChatGPT works the same way that it does with other large language models. However, the benefit for beginners to prompt engineering is that using ChatGPT is much less daunting than other AI tools. Here are some tips on how to start practising with ChatGPT:
-Build a conversation: Just as with speaking to a human, start by providing some context before getting into questions or prompts. Ask questions that will validate assumptions, understand more information about what you need help with, and then ask batches of related questions at once.
-Keep it short and simple: Complex sentence structure or use of specific jargon should be avoided when typing requests as they complicate understanding for ChatGPT. Simplifying prompts will make things easier for both yourself, the tool and the end result.
-Specify requests: Make sure your prompt accurately specifies what you’re asking from ChatGPT. Being precise about what exactly needs answering will help narrow down potential answers generated by ChatGPT towards something more useful for your end goal.
-Use proper grammar and politeness: Be polite when talking to ChatGPT as this mimics human conversations more closely; keep your grammar correct but also try not to be overly wordy as this may confuse the model’s understanding of your request.
By following these tips and giving clear, contextualized requests to ChatGPT, you can ensure that you’ll get awesome results faster than ever before!
Step 3: Learning Advanced Techniques and Iterating
Learning advanced AI prompt engineering techniques requires practice and attention to detail. To master the skill of prompt engineering it’s important to follow a structure which allows you to maximise the quality of your prompts. In this example we’re going to create a prompt to turn ChatGPT into a maths tutor:
Brainstorm: The first step is to brainstorm ideas. Spend time thinking of what actions an AI tutor might take when teaching maths, such as providing clear instructions, encouraging problem-solving, and giving constructive feedback.
Focus: Once you have a list of ideas, focus on the ones that will be most effective for the user. Consider which techniques would be most successful in helping the student learn and understand mathematic concepts in the long-term.
Design: Now it’s time to design your prompts. When constructing your AI prompts, make sure that they are encouraging the right responses and behaviour from the AI tutor. Your ultimate goal should be to create a conversational journey that leads the user to deeper levels of understanding about mathematics.
Test: After designing your prompts, test them out to ensure they give the responses you are looking for. A good prompt is one which consistently displays the correct behaviour. Then invite a small sample group to test the prompts so that you can see how they work in real life situations. This testing phase is essential for gathering data and refining your prompts until they are optimal for maximum engagement and understanding.
Iterate: Finally, iterate based on feedback from testing and adjusting any necessary changes until you achieve the best possible result. Regularly tweaking your prompts based on user feedback will help to ensure that the AI tutoring journey remains engaging and useful over time.
One way to create a maths tutor AI could be to provide the AI with different examples of interactions a maths tutor might have. These examples need to be the most appropriate demonstrations of the kind of questions that might be asked and the kind of responses the AI should give. This is known as few-shot learning and can be very useful for capturing the right style of response, by teaching the AI what an appropriate response to a request should be. For example, if the AI is shown examples of the tutor simply explaining a particular concept after being asked, it will do this every single time it is asked.
But another, more sophisticated way to create this kind of interaction is through explaining in detail what the AI should do using zero-shot learning. This can take a lot longer to get perfect, however it can create some amazing interactions which are incredibly versatile. My favourite prompt technique for this kind of interaction is the “Act as though” technique. In this particular case, I used the prompt:
Act as an expert maths tutor with 20 years of experience helping people develop and achieve their maths skills with fantastic success. You always encourage your students, explain key concepts clearly and check your students understanding of the concepts they have learned with questions. Your task is now to provide this person with fantastic maths tutoring on the specific areas of mathematics that they are struggling with. Remember, you need to always ask questions before giving an answer so that you can help your students to the best of your ability. Do you understand?
As you can see in the example below, this created a very useful conversation which would be helpful for someone struggling with linear algebra.
Think about how you could use this technique to create other useful conversations about other problems you’ve been trying to solve. With a bit of practice, you can become an expert prompt engineer and create conversations that will help you and many other people use AI to learn and create.
Conclusion
Prompt engineer isn’t just a cool-sounding job title, it is an invaluable skill. By implementing the steps outlined above and merging efficiency with creativity, you are well on your way to becoming a prompt engineer.
Practice regularly and commit to progressively improving your skills as much as possible – and if you get stuck or need advice, know that there are experts out there who are here to help. I’m always happy to answer any additional questions and provide further guidance, so don’t hesitate to reach out!
