Generative AI For Dummies Cheat Sheet

Generative AI is software that behaves unlike any other software ever known. It’s not a robot. Robots are hardware. You could think of any AI as the brains of robots, but they can exist on other hardware that is not a robot, like a supercomputer, a laptop, or an autonomous car. Think of GenAI as arguably the most creative of all AI types and certainly the easiest to use since it understands and responds to human languages.

GenAI can perform like an artist, writer, or composer to whip up paintings, stories, or music from scratch after just a quick chat about what you like or want it to do. This type of AI doesn’t just copy from its training data (although it can certainly do that, too, so watch out for plagiarism and copyright infringements); it learns from tons of examples and then generates hopefully original pieces, mixing and matching ideas and data like a chef creating a new recipe. It’s also the tech behind many of those cool apps you love like the ones that turn your selfies into cartoon characters or help you make brilliant and beautiful websites with just a few clicks!

Generative AI operates by training on huge datasets to recognize patterns and understand different forms of content. One of the star players in this field is GPT (short for Generative Pre-trained Transformer), which is a type of language model that’s really good at understanding and generating human-like text. It’s like a virtual wordsmith that can chat, answer questions, write essays, or even write computer code.

But Generative AI isn’t just about generating text. There are other options out there, like DALL-E and Midjourney, which can create images from descriptions you give it, or DeepMind’s WaveNet, which can generate life-like speech. These systems use neural networks, which are a bit like a web of digital brain cells, to learn from examples and then generate new content. They’re transforming how we create and interact with content across the board, from gaming to marketing to entertainment.

It’s important to note that GPTs are not the only GenAI game in town. For example, Claude is built on a language model similar to but not GPT (Generative Pre-trained Transformer) architectures. Claude is built on a family of large language models (LLMs) developed by Anthropic. The latest generation is the Claude 3 model family, which includes three main variants: Opus, Sonnet, and Haiku. Midjourney is another example of a GenAI tool built on a non-GPT model. Midjourney is built on its own proprietary model architecture. Other examples also are based on open-source models.

Generative AI (GenAI) is used to spur and speed creativity, innovations, and problem-solving across numerous fields. Following are some of the most widespread applications, with a few specific examples:

Content Creation

• Writing and Editing: GenAI can craft engaging blog posts for a travel website, snappy social media updates for a fashion brand, or persuasive marketing copy for a new tech gadget. It can also refine or adjust existing articles to fit different audiences or word counts. It can help write more complex and esoteric works, too, such as white papers, scientific studies, medical research studies, and other long-form works such as feature articles, analytical reports, financial reports, ebooks, and traditional books.
• Creative Writing: GenAI tools can also help write creative works such as a fantasy short story, compose a piece of ambient music for a meditation app, or generate unique recipes for a cooking blog.

Software Development

• Code Generation: GenAI tools like GitHub’s Copilot can suggest code snippets for a new app feature, find and fix bugs, or refactor code to improve its efficiency.
• Documentation and Quality Assurance: GenAI can automatically generate user manuals and other documentation for a software release or create comprehensive test cases to ensure a new video game is bug-free.

Marketing and Sales

• Inbound and Outbound Marketing: GenAI can draft compelling email campaign subject lines for an e-commerce store or produce targeted ad copy for a fitness service’s Google Ads campaign or a newsletter.
• Customer Relationship Management: GenAI-driven chatbots, such as those on a bank’s website, can answer customer inquiries about account services or suggest the best credit card options based on spending habits.

Data Analysis and Synthesis

• Summarizing Documents: GenAI can condense a lengthy financial report into a digestible executive summary or distill the main points from a series of customer feedback surveys.
• Synthesizing Information: GenAI can sift through thousands of product reviews to provide a sentiment analysis or extract the most frequently mentioned features.
• Data discovery: GenAI can find patterns in huge data sets that humans can’t and connect data points from a new perspective or to a new use. It’s so good at this that it’s a top use case for GenAI. Examples include finding new early detection methods for diseases and discovering emerging consumer trends earlier than traditional tools can.

Design and Product Development

• Generative Design: GenAI can, for example, propose a variety of smartphone case designs with optimal grip and aesthetics or generate aerodynamic shapes for a new sports car prototype.
• Fashion Design: GenAI can turn a simple dress sketch into a range of style variations or create virtual models showcasing different fabrics and outfits for an online store.

Healthcare and Pharmaceuticals

• Drug Discovery: GenAI can accelerate the search for new drugs by predicting how different molecules will interact, potentially identifying new treatments for diseases like cancer.
• Medical Imaging: GenAI can enhance MRI scans to help radiologists detect early signs of abnormalities or support the diagnosis of conditions such as fractures in X-rays.

Translation and Localization

• Language Translation: GenAI can assist in translating a user manual for a smartphone into multiple languages, though it may require human review to ensure cultural nuances are respected.

Fraud Detection and Risk Management

• Anomaly Detection: GenAI can monitor transaction data for a credit card company to spot unusual patterns that might indicate fraud or sift through insurance claims to detect inconsistencies.

These examples illustrate just a slice of the impressive range and potential of Generative AI, across industries from tech and marketing to fashion, healthcare, finance, and beyond.

When assessing GenAI models for specific types of content creation, it’s crucial to conduct a thorough evaluation to ensure that the chosen model meets your specific needs. Here’s a distilled guide to help you navigate this process:

• Understanding GenAI model types: Begin by acquainting yourself with the various GenAI models available that produce the type of outputs you seek. For example, if you want to produce images, check out DALL-E, Stable Diffusion, Midjourney, and other image generators. A multimodal GenAI tool like ChatGPT4o might serve you well, too. If you are looking to produce content in a specialized field like healthcare or customer service, check the enterprise apps your company is already using as they likely have GenAI embedded in the software that is trained to do that specific type of work. Take a look at GPTs in OpenAI’s GPT Store on ChatGPT, too, as well as the collection of specialized GenAI tools listed on services like Poe.
• If you’re looking to build a GenAI model or app yourself, check out cloud services like AWS, Azure, and Google to see which AI tools and services are easiest for you and your team to work with. Several options are available in open-source GenAI models. You might also want to consider integrating your application(s) with a GenAI model instead of building one from scratch. Look for APIs and keys like those offered by OpenAI
• If you are an individual or a small business, you might want to turn to one of the many GenAI-based chatbots on the market. For example, OpenAI’s GPTs and ChatGPT. ChatGPT Plus only costs $20 a month and gives you access to more features and access than the free version. Choose ChaptGPT Team if you want higher usage limits and to add people on the account. Team costs $25 per person/per month. An Enterprise version is also available that has far more bells and whistles in the way of features, and you can have more people on your account. You’ll have to contact the company for a quote as there isn’t a published price list for the Enterprise version. Prices, terms and features available on competing GenAI chatbots vary, and I recommend you check out several of them before selecting one or more to use.
• Identifying content requirements: Clearly define the type of content you aim to produce. Whether it’s text-based like social media posts and articles or visual content such as images, your content requirements will guide you in selecting a GenAI model or application that can effectively fulfill those needs.
• Evaluating performance metrics: Consider important performance indicators such as the relevance and quality of the generated content, the diversity of output, the speed of content generation, and the model’s ability to capture complex details. Evaluating these factors are essential in determining the suitability of a GenAI model for your content creation tasks.
• Testing for fit: When you have narrowed down which GenAI models are most likely to do the job you want them to do, try them out and see how they fit. Most GenAI models have a free version or a free trial period. Even though the freebie versions may not contain the same capabilities as the premium versions, you can still get a feel for how each model fits your needs overall.
• Considering integration: Assess the ease with which the GenAI models can be integrated into your existing workflows. A smooth integration process is key to streamlining content creation and enhancing the efficiency of producing various content types.
• If you don’t need to integrate GenAI with any other apps, you still might want to use integrated or embedded GenAI models. For example, consider using ChatGPT Plus to generate your text content and summoning the GPT Image Generator to build illustrations for you — while you’re still working in ChatGPT Plus. You can also find GenAI embedded or integrated in commercially available software that you’re probably already using, or that you can easily obtain. You’ll likely find software that is specialized for tasks in your industry or the work you want to do with GenAI to be particularly helpful.

• You may also want to consider copying and pasting the GenAI output into other software where you can edit and format the content more easily as well as distribute it. Most people port or copy & paste outputs or integrate GenAI models for just this very reason.

• Efficiency and time savings: Determine the extent to which GenAI models can automate manual content creation tasks. This can free up your team to concentrate on strategic and creative aspects of content development, saving time and boosting productivity.
• Ethical considerations: Finally, ensure that the GenAI models comply with ethical standards concerning data privacy, security, transparency, explainability, and intellectual property rights. Adherence to these guidelines is critical when creating content. Be sure to check and see whether your prompts and responses are retained by the GenAI model maker to train other GenAI models as this can expose proprietary data or company secrets.

By following this guide and considering these key aspects, you can effectively evaluate how well different GenAI models meet your specific content creation needs.

Whether you’re looking to refine your GenAI-generated content or unlock new levels of creativity, mastering advanced prompting techniques and a few other tactics is how you get the content you want. From stitching together seamless narratives to orchestrating the work of multiple GenAI tools, each method gives you a different kind of control over the content you are creating with GenAI.

Here are some ways and tips that will elevate you from a casual user to a skilled prompter and GenAI master:

Output Stitching

• Definition: This technique involves combining the outputs from more than one GenAI tool and manually combining the bits and pieces to create a cohesive final product. Or, you can also use this method to complete a work from chunk writing (working on one section at a time of a longer piece so as not to confuse the GenAI and get better results).
• Example: If you’re generating a long-form article, you might first ask the GenAI to outline the piece and then generate each section individually, and finally, stitch them together in a coherent structure. Or, you can give the same prompt to two or more GenAI tools and stitch together the bits and pieces you choose from each to create a better output than you got from any one of them alone.

AI Aggregation

• Definition: AI aggregation refers to the process of collecting and synthesizing information or responses from multiple GenAI models or sources and using them collectively in a unified work.
• Example: When looking for comprehensive information on a topic, you might prompt different GenAI models for their insights and compile the responses to form a well-rounded view. Or, you might use one GenAI model to write a blog post, another to generate an image or illustration, and another type of software with embedded GenAI to create a video to embed in that same post, too.

AI Chaining

• Definition: AI chaining is the sequential use of GenAI outputs as part or all of inputs for other GenAI tools.
• Example: To make a computer game, a training video, or a movie film, you might prompt one GenAI tool to create character profiles and then use the output from that as the main substance of a prompt that you use in another GenAI tool to build a storyboard, generate a storyline, and draw key scenes. Then you might continue with subsequent prompts in yet another GenAI tool to flesh out a full narrative in an actual script. You can change the order of these steps, if you like. The point is that you are using multiple GenAI tools to do specialized tasks and then using those outputs as part of or the main thrust in prompts for other GenAI tools that are specialized in the next step or task you need to complete to eventually arrive at a finished, unified work.

Prompt Chaining

• Definition: Prompt chaining involves using the output of one prompt as part of the input for the next in the same chat on the same GenAI tool, creating a series of linked prompts and responses.
• Example: If you’re designing a product, start with a prompt to generate a concept, use the response as a base for the next prompt to refine the design, and continue until you reach a final detailed blueprint.

Prompting with Different Roles in the Same Prompt

• Definition: This method assigns different roles or perspectives within the same prompt to generate diverse and multi-faceted content.
• Example: When creating a training dialogue, you might prompt the GenAI to adopt the roles of both a customer and a customer service representative to simulate a realistic conversation. Another example is to form a virtual committee of experts. test audiences, communities, voters, users, and any mix of people and experts you like as a command in your prompt. The response will deliver answers and interactions from these different roles and personas to further enlighten, inspire or deliver divers insights for your work.

By leveraging these advanced prompting and methods, you’ll not only refine the quality of the content produced but also expand the capabilities of the GenAI to meet your more complex and nuanced demands.

The key to successful prompting lies in clarity, specificity, and creativity — so experiment freely and watch as your prompts bring forth GenAI-generated works that are truly useful and better matched to your needs and goals.