Prompt Engineering Guide: How to Engineer the Perfect Prompts

Effectively implementing Artificial Intelligence (AI) capabilities into a project or product is challenging. One of the biggest challenges is crafting perfect prompts for an AI agent. For example, if you’re working on a conversational AI, do your prompts communicate to the AI the kind of responses you want? Does it deliver the style, information and length that you want from the AI generated response.

Having a strong writing background can help when it comes to designing better prompts. In this article, we will explore how to properly engineer the perfect AI prompts so that they are successful.

By following the step-by-step guide with examples and tips, you will have everything you need to craft a well-rounded set of AI Prompts that are optimized for the best quality AI generated content.

What is Prompt Engineering in AI

Prompt engineering is an essential concept for natural language processing (NLP) in artificial intelligence. It is based on the idea that tasks can be better articulated using prompts instead of implicit descriptions. This involves converting tasks into prompt-based datasets and training a language model with this “prompt-based learning” method.

Recent advancements in prompt engineering have focused mainly on GPT-2 and GPT-3 language models. 2021 has seen success with multi-task prompt engineering being used on various NLP datasets, while there has also been promising applications with prompts that contain a train of thought in few-shot learning examples. The use of text prepended to the prompt may help to improve zero-shot learning (no examples given to the AI) by developing a chain of thought.

Open source notebooks, community projects, and image synthesis tools are increasing accessibility to these tools as well. By February 2022 over 2,000 public prompts were available for around 170 datasets. This opens up further possibilities for prompt engineering as researchers have access to more data sources than ever before exploring new applications for this technology.

NLP prompt engineering

There are many Large Language Models (LLMs) for AI text generation available today, the majority of the tools run on OpenAI’s GPT-3. However, there are other high quality LLMs available, including a few open source tools. Three of the most well known tools are:

1. GPT-3: OpenAI‘s Generative Pre-trained Transformer

GPT-3 is an advanced language model created by OpenAI, a research company backed by prolific investors Peter Thiel and Elon Musk. This third generation of OpenAI’s language models is capable of generating human-like text from just a few examples, making it a powerful tool for writers and researchers alike.

To create GPT-3, OpenAI collected 570 GB worth of data from the internet, including all of Wikipedia! This gave it an immense database to work with; in fact, the Model includes 175 billion parameters – that’s more than 20 times greater than the 11 billion parameter count found in its predecessor.

GPT-3 works by understanding how words relate to one another, learning the context and structure in which they are used then leveraging its own vast database to generate accurate responses. It can even generate full essays quickly and accurately, making it a valuable asset for writing tasks of all sizes. This makes GPT-3 much more efficient – and reliable – than other language models currently on the market.

ChatGPT is an AI-based chatbot from OpenAI which was launched in November 2022. It boasts an impressive ability to understand natural language, thanks to its foundation based on the GPT-3 family of large language models. Utilizing both supervised and reinforcement learning techniques, it has been optimized to provide ultra-realistic dialogue simulation that has never been seen before.

Sentient machines have been a thing of science fiction for decades, but with ChatGPT, OpenAI brings this dream closer to reality. In addition to understanding natural speech, ChatGPT also enables users to ask multiple questions at once and receive accurate responses quickly. This breakthrough technology allows conversations with chatbots that feel amazingly humanlike compared with traditional bots or voice assistants.

What makes ChatGPT so unique is its capability to learn without requiring any additional input or being re-programmed by humans — it truly stands out among other AI-based chatbot solutions for self-learning capability. By constantly analyzing communication patterns of real human conversations, ChatGPT continues to evolve and improve itself in real time – a feat not many other AI technologies can achieve today. This makes it much more reliable than conventional bots designed specifically for certain scenarios, as ChatGPT can readily be adapted into any context as needed at scale across a wide range of businesses and markets worldwide.

2. Jurassic-1 Jumbo: AI21 Labs‘ Largest Language Model

Jurassic-1 Jumbo is an innovative and powerful NLP-as-a-Service developer platform created by AI21 Labs, a cutting-edge Israeli artificial intelligence company specializing in the area of natural language processing. This revolutionary new offering stands out as the largest language model ever released for general use by developers everywhere (although GPT-4 is expected to be much larger), boasting an impressive 178 billion parameters. It can be accessed through AI21 Studio, a user-friendly website and accompanying API that enables developers to craft comprehensive text based applications.

Developed using a gargantuan dataset encompassing 300 billion tokens sourced from English websites such as Wikipedia, news sources and OpenSubtitles, Jurassic-1 Jumbo excels with its remarkable lexical capacity of 250,000 items – an unprecedented 5 times more than other language models at the time of its launch. Intuitive yet incredibly powerful, it promises to revolutionize the development industry providing developers with access to efficient and effective technical resources.

3. GPT-J: The most advanced open source Large Language Model EleutherAI

GPT-J is a cutting-edge transformer model created by EleutherAI, providing the most advanced open sourced alternative to OpenAI’s GPT-3. It has the capability of performing a wide range of natural language tasks and is built with Ben Wang’s Mesh Transformer JAX,. The tokenization vocabulary consists of 50257 tokens that employ the same set used in GPT-2/GPT-3.

GPT-J was trained on Pile, an immense datasets by EleutherAI, using 402 billion tokens across 383,500 steps on a TPU v3-256 pod. As it stands today, GPT-J can be used without further training to solve multiple language solutions, or it can undergo fine-tuning for specialised applications. Therefore GPT-J provides maximum flexibility from Day One.

If you’re wanting to use GPT-J to generate content, then you will be happy to know that there’s a great deal available to use GPT-J as much as you want. You can use GPT-J with unlimited generations free with all subscriptions. also allows you to connect to OpenAI, AI21, Aleph Alpha and other top quality LLMs using your own API keys and build your own custom AI prompts for high quality AI text generation. You can also get high quality images using Stable Diffusion and Dall-E through the Riku platform.

Prompt engineering is a crucial skill to help maximize the potential of LLMs. These models are specifically trained with large datasets to answer questions, produce content, make reports of meetings, write down computer programs and much more.

The process of prompt engineering is essential for getting the best results in an LLM. By using specially designed prompts relating to precise desired tasks, natural language processing models can become fine tuned and better understand what output needs to be generated. This will open up a gigantic realm of use cases regarding text and code creation.

Instruction-following models require particular prompts that have gone through deliberate design strategies in order for them to generate more precise output data. Each use case needs distinct prompts developed from careful analysis as this makes it possible for a model’s capabilities to be extended beyond what was previously thought feasible.

Essentially, prompt engineering techniques enable LLMs to comprehend questions and other requests related to text or code generation thoroughly before delivering the desired result. By having the right prompt format available, these vast language models can reach their full capability and expand our understanding of how helpful they can be when solving various tasks.

Here is a guide to help get you started with prompt engineering.

1. Include Direct Instructions in Prompts

In order to ensure that natural language models (LLMs) understand the task they are being asked to complete, it is important to provide direct, clear instructions. This can be done by including words like “Translate” in the prompt, followed by an English phrase that needs to be converted into a different language. It is also helpful to set aside a designated area for GPT-3 to provide the translated sentence or phrase in its preferred language.

By constructing prompts with these direct instructions, natural language models will be able to recognize the task at hand and answer correctly. For example, if you wish for GPT-3 to translate a sentence from English into Spanish, your prompt should include “Translate” followed by the English phrase and then make room for GPT-3’s response in Spanish. By following this approach, you can make sure that GPT-3 understands your request and duly provides an accurate and grammatically correct response.

2. Give Examples for Better Responses

If clear instructions are not enough to ensure that a language processing model (LLM) correctly understands the task at hand, providing examples can be a beneficial way of eliciting better results. This technique helps to train the LLM by showing it how to perform a desired action based on a given sample.

For example, presenting GPT-3 with an example English sentence and its translation in Spanish can help it become familiar with the corresponding words and syntax, as well as understand patterns from context. This example can then serve as a template for the LLM to accurately translate other similar sentences. In this way, providing examples builds on the knowledge that the model already has acquired and leads to more accurate results.

Whilst zero shot learning leaves the AI guessing at the kind of response you want, providing examples aims to put the AI into a pattern of returning the kind of responses you want. One shot learning is when you provide one example of the content you want. Finally, few shot learning is where you give the AI multiple examples in your prompt.

Overall, this method demonstrates that presenting meaningful examples is an effective approach towards training language models for improved results.

3. Validate Outputs

It is essential to validate the output of an LLM (Learning Logic Model) as even small mistakes can be costly. Checking the model’s outputs and providing feedback when it produces incorrect responses is crucial for its development. This will help the model refine its performance as it learns from each mistake and ultimately yields more precise results in the long run.

The process of validating an LLM requires critical thinking and analysis, as each incorrect response needs to be carefully considered before providing feedback. During this process, it’s important to identify potential underlying issues such as flaws in data or bias in the machine learning algorithms used by the model that may have caused inaccurate results so that these problems can be addressed and prevented from occurring again.

OpenAI has identified several strategies that can be used to ensure efficient training of NLP models when constructing prompts. For example, the latest versions of their models, such as ‘text-davinci-003′ and ‘code-davinci davinci-002′ recommend including all pertinent information at the beginning of the prompt, including any context or desired outcome, as well as length, format style etc.

Including examples of expected output beforehand is also recommended for optimal performance. Not only do these aid in programmatically parsing out multiple outputs reliably, but they also give the model a greater boost by providing specific formats it can use to form its own generated text or code.

Finally, using descriptive language combined with specifics rather than imprecise statements is essential if you want to get the best possible result from your model. Doing so will reduce the task being asked from it while still keeping it focused on achieving its intended outcome.

Image generation AI

AI based image generation is rapidly gaining popularity within the digital art sector. AI model algorithms allow users to generate realistic images that appear as if they were created by hand. This quick and effortless approach facilitates the development of stunning imagery in mere moments, eliminating the need for tedious manual processes traditionally associated with image creation.

Encompassing a variety of techniques, such as generative adversarial networks (GANs) and style transfer, AI models can conveniently create complex works of art that make full use of color, form, texture, and other elements with astounding accuracy. For example, GANs “learn” from training data provided by humans so they can produce original images from scratch or mimic existing styles with remarkable precision. Meanwhile, style transfer lets users apply stylistic choices to pre-existing images in order to customize them to their liking without having to manually manipulate each component of the image separately.

This revolutionary method makes art production far more accessible than ever before. Whether it’s creating unique designs for illustrations or logos or applying intricate filters to photographs, AI-generated digital artwork can bring any creative vision to life quickly and effectively without needing significant experience or investment in expensive software tools.

The three most popular AI image generation tools available today are:

1. DALL-E 2: Developed by OpenAI, this is an easy to use tool that requires you to sign up and confirm your phone number via SMS. You get 50 credits for the first month free, with 15 additional credits each month thereafter, with more credits purchasable. You are able to produce pictures by typing in a prompt on the Dall-E website and it will produce the image for you in seconds.

2. Stable Diffusion: This is an open source project and you can run it on your own machine if you have time and a beefy GPU. Alternatively, you can use the DreamStudio which offers a number of free trial credits and more purchasable ones. Stable Diffusion is also available on Riku.AI and a number of other AI text generation platforms. It is able to produce stunningly high quality images in multiple art styles and even photorealistic images. The images in this blog post have been produced using Stable Diffusion.

3. Midjourney: Based on the Discord platform, users are given a number of free credits to try things out with more available for purchase. To access Midjourney, you need to follow them on Discord, join the appropriate channel then send them a series of slash commands.

As the digital art community continues to evolve, new technologies are providing immense opportunities for creativity. Image generation AI has revolutionized this space, allowing even novice users to create impressive works with little effort. While this is undoubtedly a great convenience, there are some ethical considerations that come with using image generation AI.

First and foremost, it is important to take care when creating prompts for image generation AI so as not to mislead consumers into thinking that certain images were created by real artists. For example, naming a particular artist in the prompt could lead to confusion in search results if more AI-generated images for that artist appear than actual works produced by them. This can make it difficult for consumers to know which pieces have been made by humans and which ones have been generated by an algorithm. As such, it may be best practice to refrain from referencing any particular artist or style of artwork when creating these prompts. It is also worth noting that living artists often depend on selling their artwork in order to make a living, so if their real images become overtaken by AI images they could lose out financially.

Apart from avoiding references to real artists, it is important to provide clearly laid out instructions when creating prompts for image generation AI. Be sure to include relevant details such as hairstyle or clothing style if desired, so that the model understands exactly what types of your desired results outputted from the prompt. Additionally, keep in mind other best practices from natural language processing (NLP) engineering when crafting prompts – using natural language rather than technical jargon is key for producing effective and comprehensible strategies. Testing the prompts carefully after drafting them will allow engineers to ensure quality results every time they use their AI toolkit for digital art creation..

Human evaluation and feedback

When releasing prompts for other people to use, it is essential to listen closely to the feedback provided on the texts or images created with your prompts. After all, the purpose of prompt engineering is to enable AI models to generate the kind of content a user wants. In order for users to be satisfied with their results, they should receive results that align with what they are searching for; if a person is expecting blog post-style content and then gets text written in the same style that a BBC News presenter might use, they are unlikely to be pleased.

This brings us to an important point: One of the motivations why AI companies are transitioning away from using humans as evaluators when assessing generated text is because human assessors may not be able to accurately tell machine-generated writing apart from human-written text. This was proven in a recent study conducted by research institutions such as the University of Washington and the Allen Institute for Artificial Intelligence. The report discovered that untrained human assessors tend to more heavily focus on surface features when gauging how “human-like” a certain piece of writing is—therefore leading them to have both low recall and F1 scores in terms of their judgments.

Consequently, seemingly random text streams (such as blogs which deviate into unrelated topics) can actually be what someone is actually looking for when obtaining pieces of content. It’s important that these things are taken into account when analyzing feedback concerning output creativity with your AI prompts; sometimes guidance needs to be given regarding steering these algorithms towards the ideal style which corresponds with whatever it is you are aiming at producing!


In conclusion, creating the perfect AI prompts is not a simple task, but with the right approach and resources it can be done. Taking into account user intentions, industry best practices and the latest in AI technology can ensure that you create prompts your users will love.

Creating AI prompts is a process that requires thought and planning to get just right. However, with a little time and effort you can engineer the perfect AI prompt system that users will love interacting with. The end result will be worth it, so don’t underestimate how much of an impact AI prompts can make on your product or service!

Over the next few weeks I will be putting together how-to’s on prompt engineering that will help you take your AI prompts to the next level. If you want to join along with these, you can pick up a subscription to Riku.AI which allows you to use many different AI API’s along with unlimited GPT-J generations. If you choose to purchase through my link you won’t be charged any extra but you will be helping to support the website.

Leave a Comment

Your email address will not be published. Required fields are marked *