Large Language Models (LLMs): Text in, Text Out (note, Code is also a language). Examples include GPT4, GPT3, Charlie 1, Claude 3.5

Diffusion Models: Typically text to multimedia like images, video, audio. Examples include Stable Diffusion, Flux, Stable Video, midjourney

Text to Speech (TTS): Going from Text to Audio. Examples include ElevenLabs

Audio to Text: Going from Audio or Video with audio to text. Examples include OpenAI Whisper

Multimodal Models are different in that they typically can understand multiple modalities of data as inputs, and create multiple modalities. Most multimodal models are currently just different models stitched together with Langchain or other language driven architectures.

Cues are typically embedded in the prompt to guide the model’s output without prescribing a specific framework. They are helpful for fine-tuning the response to fit particular needs or contexts.

Cues are more specific signals or prompts used within those methods to refine or direct the response further.

Cues are elements within those strategies that further direct or fine-tune the response.

Factual Information:
Cue: “According to recent studies…”
Example Prompt: “According to recent studies, how does sleep affect cognitive performance?”

Summarization:
Cue: “In summary,”
Example Prompt: “In summary, what are the main arguments presented in the article about climate change?”

Opinion or Perspective:
Cue: “In your opinion,”
Example Prompt: “In your opinion, what are the most significant challenges facing remote work?”

Step-by-Step Instructions:
Cue: “First, then, finally,”
Example Prompt: “Explain how to bake a cake. First, list the ingredients, then outline the preparation steps, and finally describe the baking process.”

Comparison:
Cue: “Compare and contrast,”
Example Prompt: “Compare and contrast the key features of electric cars versus hybrid cars.”

Definition or Explanation:
Cue: “Define,” or “Explain,”
Example Prompt: “Define blockchain technology and explain its potential impact on financial transactions.”

Clarification:
Cue: “In simple terms,”
Example Prompt: “In simple terms, how does the internet work?”

Creative Response:
Cue: “Imagine if,”
Example Prompt: “Imagine if humans could live on Mars. Describe what daily life might look like.”

Positive Emphasis:
Cue: “Highlight the benefits of,”
Example Prompt: “Highlight the benefits of using renewable energy sources over fossil fuels.”

Neutral Tone:
Cue: “Objectively discuss,”
Example Prompt: “Objectively discuss the pros and cons of implementing universal basic income.”

Historical Context:
Cue: “Historically,”
Example Prompt: “Historically, how have major technological advancements influenced job markets?”

Speculative Scenario:
Cue: “What if,”
Example Prompt: “What if artificial intelligence could fully replicate human emotions? How might this affect human-robot interactions?”

ChatGPT and similar tools like Bard, Anthropic’s Claude, and Bing Chat utilize generative AI (GAI) technology to quickly produce coherent language in various styles, formats, and tones. GAI technology leverages large language models (LLMs) trained on huge datasets to generate human-like text, making it useful for content creation, communication, and creative writing tasks. GAI-powered chatbots can produce comprehensive, cohesive essays, catchy social media blurbs, or detailed outlines in seconds.

OpenAI, the organization behind ChatGPT, has not publicly disclosed the specifics of the individual datasets used, however, it’s known that the model was trained on a diverse range of text sources, which includes a broad spectrum of publicly available information on the internet.
On May 16, Reddit announced a partnership with OpenAI to provide its content to the widely-used chatbot, ChatGPT. This news led to a 12% increase in Reddit’s shares during extended trading. The collaboration highlights Reddit’s strategy to expand beyond its advertising revenue, coming on the heels of a recent partnership with Google to make its content available for training Google’s AI models or Google’s SERPs. Similarly, ChatGPT has also teamed up with LinkedIn Pulse and Quora, integrating their articles into its training dataset.

Generative Engine Optimization (GEO) is an emerging term in the digital marketing landscape. Similar to Search Engine Optimization (SEO) and App Store Optimization (ASO), GEO involves optimizing content specifically for AI-driven search engines, known as generative engines. These engines, powered by large language models (LLMs) like ChatGPT, synthesize information from multiple sources to deliver comprehensive and personalized responses to user queries.

Generative engines excel at creating cohesive and innovative content by synthesizing vast amounts of information. By building a micromonopoly, you create a specialized niche where your content becomes the authoritative source. Surround Sound SEO complements this by ensuring your content appears across multiple top-ranking pages, rather than just aiming for the top spot on search engines.
Combining the principles of building a micromonopoly with the theory of Surround Sound SEO offers a robust strategy to enhance Generative Engine Optimization (GEO). This approach not only helps you dominate a niche but also ensures that your content is highly visible and consistently utilized by generative engines.

Methods are systematic approaches or frameworks for crafting prompts that yield more structured, relevant, or insightful responses from ChatGPT. They help users get the desired output by applying a predefined strategy or format.

Methods are broader strategies or frameworks that provide a structured approach to interacting with ChatGPT.

Methods define the overall strategy or approach for engaging with ChatGPT.

Cues and methods complement each other. Methods provide a broad framework for the interaction, while cues are used within these frameworks to guide the response’s specifics, ensuring that it aligns with the user’s needs and expectations.

Non-linear marketing is a transformative approach where businesses interact with their audiences in a more dynamic, adaptable way rather than following a traditional, fixed sequence of steps. Unlike the linear model (where customers are guided through a set path, such as a click-through to a form), non-linear marketing allows for flexible paths in lead generation, capture, and nurturing. AI-driven methods, such as chat interactions, exemplify this by enabling conversations where both the business and the customer can deviate from a fixed script, accommodating a more natural flow of questions, answers, and information.

Non-linear marketing has become popular recently due to the rise of AI and conversational technologies that can engage customers in flexible, personalized ways. These technologies allow businesses to support more interactive, fluid customer journeys that adapt to individual behaviors rather than forcing them through a rigid series of steps. This shift is significant as AI-powered conversations are becoming more prevalent in customer engagement, making it essential for marketers to integrate and understand non-linear marketing approaches.

The term “non-linear marketing” is relatively new in its current context, especially as it pertains to digital interactions and AI-powered lead generation. Although concepts of flexibility and personalization have long been part of marketing, non-linear marketing specifically reflects a shift away from traditional, sequential funnels to more adaptable engagement models driven by recent advancements in AI and conversational marketing tools.

Yes, AI chatbots are particularly well-suited to support non-linear marketing due to their flexible, conversational capabilities. Here’s how they can enhance non-linear marketing:
1) Adaptable Conversation Flow
AI chatbots can respond dynamically to user inputs, allowing customers to ask questions or request information out of a pre-set sequence. For example, instead of following a rigid, scripted path, chatbots can adjust to the customer’s unique inquiries or concerns, maintaining engagement without requiring them to follow a traditional funnel. This flexibility is at the core of non-linear marketing.
2) Real-Time Personalization
Chatbots can collect data during interactions and instantly adjust responses based on the user’s preferences, behavior, or location. This personalized, in-the-moment customization provides a more relevant experience, moving away from a “one-size-fits-all” approach and letting the customer drive their journey more organically.
3) Continuous Lead Nurturing
AI chatbots enable continuous lead nurturing by adapting to a customer’s stage in their journey. They can respond to new queries, pick up on previous interactions, and offer different types of information depending on the customer’s current needs, which enhances the non-linear nature of the customer experience. This flexibility means that the interaction can flow according to the customer’s needs rather than a preset linear funnel.
4) Handling “Off-Script” Moments
Unlike traditional scripted interactions, AI chatbots can handle deviations without derailing the conversation. For instance, if a customer asks product-specific questions during a more general onboarding process, the chatbot can accommodate these “off-script” moments, provide relevant answers, and then naturally guide the customer back to the core engagement path.
5) Collecting Feedback and Iterating on Engagement Paths
AI chatbots also collect data from each interaction, allowing businesses to understand common deviations or questions that users bring up. This helps marketers refine the bot’s responses and engagement strategies to align even more closely with customer needs, continuously enhancing the non-linear experience.

A prompt is a set of instructions or questions provided to a person or a system to elicit a response. In the context of a conversational AI, a prompt is the user’s input, which could be a question, command, or any statement that the AI responds to. In a prompt, I could:
* Ask to get an answer
* Ask to check the correctness of my prompt (hence refining the answer)
* Ask to help me compose the question in case I don’t know how to do it in an optimal way (hence to generate the prompt)

Prompt engineering is the art and science of designing inputs, known as prompts, to obtain desired outputs from AI models, particularly generative AI.

Most prompts work similarly across major generative AI platforms because fundamental elements like “cues” and “methods” are largely consistent. This means that a prompt used in ChatGPT will typically function similarly in Microsoft Copilot or Google Gemini. However, subtle differences may exist in how each platform interprets certain inputs, so it can be beneficial to adjust prompts slightly to fit the specific nuances of each AI system for the best results.

AI can be a valuable tool for working with prompts by:
* Assisting in prompt creation: AI can guide you in structuring your prompt to ensure clarity and relevance for the task at hand. Use the prompt generator method.
* Verifying prompt accuracy: AI can evaluate your prompt for potential issues, such as ambiguity, incomplete details, or incorrect phrasing.
* Enhancing and optimizing prompts: AI can suggest improvements or alternative phrasings to make your prompt more effective and aligned with your desired outcome.