Prompt Engineering for Generative AI summary

Let’s now unpack the fourth of the authors’ prompting principles: evaluate output. For one-off prompts that you won’t revisit again, simple trial and error is usually enough to get you where you need to be. This is known as blind prompting. But if you’re going to use your prompt repeatedly or you’re building an application based on a prompt, you’ll need to go a little bit further. 

In many cases, the solution for evaluating output is pretty simple: a thumbs-up/thumbs-down rating system. Take a selection of prompts you want to test and run them each through your AI model multiple times. Then lay out the outputs next to each other in a spreadsheet and give them a thumbs up or a thumbs down. If you want to get more fine-grained, you can also use a numerical rating score instead of thumbs. It’s also possible to automate the spreadsheeting part using the OpenAI Python package, though we won’t be able to cover the exact steps here. 

It’s a general rule that you can get more out of something if you understand how it works. So, let’s now embark on a daunting task: understanding large language models, or LLMs for short.

It all starts with what’s called the token. This is the fundamental unit in natural language processing – or NLP. Tokens can represent sentences, words, or even sets of characters which form subwords. For a rough idea of size, a text of 100 tokens is about 75 words long. 

Before you can prompt your LLM, you need to choose which one to use in the first place. There are many models to choose from, each with their own computational architecture, strengths, and weaknesses. Of course, you have the big powerhouses like Open AI’s ChatGPT, Anthropic’s Claude and Google’s Gemini, but there are also smaller models like Meta’s Llama and even Mistral, a model by French start-up Mistral AI.

The surest bet here is to run the same prompt through several of these LLMs and see which output works best for you, all for your specific task. 

They say a picture is worth a thousand words. Well, with AI, you can generate thousands of pictures with just a few words. But how exactly does it work?

The most popular AI models that generate images from text are called diffusion models. As of late 2025, these include OpenAI’s GPT Image, Google’s Nano Banana, and open-source alternatives such as Stable Diffusion.

We’ll finish with a couple of tricks of the image-generating trade. One great tool when using diffusion models is reverse engineering prompts. Sometimes you might know what you want your output to look like, but you don’t know quite how to put it into words. Luckily, models like Image GPT and Nano Banana allow you to upload an image and ask it to give you its description. You can then take this prompt and tweak it to get your desired output.

You can also include quality boosters in your prompt. These are simple expressions like “beautiful”, “high resolution” or “trending on ArtStation” that can be included in your prompt to improve the quality of the resulting image.