Prompt engineering is the process of guiding generative models to best produce your desired outputs. To be honest, not enough is yet known about it to really be called “engineering,” so sometimes we just say “prompting.” This is a list of information and resources to get you confidently developing prompts which consistently and reliably deliver you the written content you are looking for.

Our end goal is to create something called a stable prompt, which is a prompt which reliably delivers you the content you want even if you modify certain key words within the prompt. If you are repeating a task in which you are using prompts to achieve a single goal with some variation, then it will be worthwhile to invest effort into creating a stable prompt. Unlike a disposable or a one-time prompt, a stable prompt is something you can re-use over and over and get a reliable output. These prompts should be stable enough that you can modify a few key words, and it will still deploy content of equal quality and do so reliably. Creating a stable prompt specifically for your needs is what prompt engineering is all about.

While writing up your prompt, try specifying the following characteristics to guide the AI into creating what you want (these work for instruct models only, not completion models):

We recommend using the Library of Babel to document your prompt engineering journey. This tool allows you to build a repository of effective prompts, track your progress, record your successes and cross-pollinate them within a growing community. By creating this valuable resource, you can contribute to the collective knowledge in the field of prompt engineering and share your insights with the broader AI community.

Here we will delve into different approaches you can take to enhance the output of your AI models. These principles will provide a solid foundation for improving the quality, relevance, and accuracy of the AI's output. Prompt engineering is a blend of art and science, involving a deep understanding of language nuances, context, and the specific capabilities of the AI model.

Few shot learning means you give the model a couple of examples and it riffs off of those to create the content you are looking for. In these examples, you want things to be as consistent as possible, with style, time frame, length and other features remaining similar to each other across examples. The only thing you want to be different from example to example is the part you want the generator to innovate on.

The example set offers guidance to the model as to what elements within the generated content should emulate the style of the example. Simultaneously, the elements which are different from example to example signal to the model that this is the feature which it should specialize to what you specify. What is different from example to example is what the model will innovate on in what it generates.

Example: I know this sounds like a difficult concept to wrap your mind around. Let me give an example. So for me, I love Pablo Neruda poetry. Neruda is a Chilean poet and one of the first things I ever did with generative AI was make a poem generator replicating his style. How did I do that? I started by taking a couple of real Pablo Neruda poems. Neruda's poems are especially well suited for this exercise because he has a collection of poems which all start with the title "An Ode to <something>" and then he writes a poem about that topic.

In the worksheet, you can find three of these poems. So we have one poem actually written by Pablo Neruda called "Ode to My Socks" and an "Ode to an Artichoke" and so on. These examples are perfect because they all start with the same title, and the poem style and theme is consistent across examples. All of them are dedicated to the object which is unique from title to title - be it socks, or artichokes or whatever.

So because we offered multiple examples which start with “Ode to <Blank>” the model will know to start with “Ode to” in the title of poem it generates, and because they all are written in short stanzas and fixated on the subject of the title, the model will know to repeat those elements too. But because they all are ode to different objects, the model will likewise differentiate its output in regard to what the ode is about. The subject it will dedicate the generated poem to is whatever you put in the title after “Ode to…”. So once you set up something like this, you can create a poem generator which will spit out an ode to whatever subject you choose to specify, while keeping stable elements such as style and length in the style of a Neruda poems.

Try it yourself: we have a very convenient platform for you to test this process. Head over to an AI platform we developed exclusively for this, called Mesopotamia.ai. Once you created an account, head over to “Text” in the top right corner, and then from the menu which appeared select “Create”. What you will see is pre-created slots where to put examples, and then a slot where to specify a title. After you’re done, specify a title on a subject matter that you want to generate content on. What you will get is content on the subject matter of the title, but in the style and format of the examples you offered. The separate boxes for examples and titles is just training wheels. You can re-create this process in ChatGPT or any other model by copying and pasting the content all together into the prompt window and ending with “Now generate content similar to the examples but on the subject of <your subject matter>”

Mesopotamia.ai - go to Text>>>Create and try few shot prompting in this training ground we created for you.

In Chain-of-Thought prompting, you are achieving your end product through multiple steps which feed into each other. So you would create a prompt for one step, and then use the output of that step as part of a prompt for the next step. Amazingly enough, even though you are not providing the model with anything it didn’t already know, prompting it to generate guidelines for itself is incredibly powerful in improving its output.

Example: Let me give an example to describe how this is done. Let's say you want to write a story about a subject like cats in the style of Ernest Hemingway. Rather than listing out what characteristics are typical to Hemingway’s writing by yourself, and then you ask the AI to do it for you. So you would start a chain of prompts where the first link is: “List the characteristics of Ernest Hemingway’s writing”, and then step 2: “Write a story about cats with the characteristics listed above.” This works because the model actually takes everything written and generated beforehand as part of the prompt. It doesn’t just read your last prompt, but considers everything written in the thread prior as part of the prompt.

Try it yourself: Use the worksheet provided below and fill in the blanks. This is a more complicated example of a chain of prompts.

Worksheet for Chain-of-Thought prompting

You see here that we offer the example: “List five <some ideas> on <a topic of your choosing>”. In the spaces blocked off by carrot brackets you can specify any topics you want. After you shape that first prompt and have the model respond to it, repeat the process again with an adjacent idea and topic. And then as your third prompt input you would ask the model to connect the elements from the first list and the second one. So you would it ask it to connect one of the concepts from list one and one concept from list two.

If you are working with a developer API, such as platform.openai.com, you will get a bunch of toggles which will affect the output. The terminology here is quite universal across models and providers.

Additional resources