Artificial Intelligence All-In-One For Dummies Cheat Sheet

Although artificial intelligence (AI) has been around in some form since the 1950s, it was the first public release of ChatGPT and OpenAI’s foundational large language models (LLMs) like GPT 3.5 that made generative AI (GenAI) applications available to people who aren’t data scientists and computer geniuses.

OpenAI’s models and models from companies such as Google, Facebook, Anthropic, and others ignited the world’s demand for increasingly sophisticated GenAI models and tools, and the market was quick to deliver. But what’s the use of having so many GenAI tools if you get stuck using them? And make no mistake, everyone gets stuck quite often!

This cheat sheet facilitates the very best results by introducing advanced (but pretty easy) prompting techniques and offering useful tips on how to choose AI models or applications that are right for the task.

What AI is, in brief

Artificial intelligence (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. AI software comes in many types from movie recommendation systems to self-driving cars.

But even more interesting, generative AI (GenAI) is the most creative of all AI types. It 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 when using GenAI software)
• Learns from tons of examples and then generates hopefully original pieces, mixing and matching ideas and data like a chef creating a new recipe.
• Is also the tech behind many of those cool apps you love, like the ones that turn selfies into cartoon characters or help 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. Some  other options out there include

• Neural network-based software: 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.
• Other non-GPT models: 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.

How to apply GenAI for common tasks and use cases

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.

How to choose GenAI options

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.
• 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.
• 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.

How to prompt AI and apply other methods and tips

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 techniques and tips that will elevate you from a casual user to a skilled prompter and GenAI expert:

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 diverse insights for your work.

Leveraging these advanced prompting methods helps not only refine the quality of the content produced but also expand the capabilities of the GenAI to meet more complex and nuanced demands.