AI

An employee gains the superpower of templates for completing tasks. They select one for responding to customer inquiries and generate multiple

Maximize Productivity with ChatGPT & Template-driven Conversations

Making the Most of ChatGPT with Templates Is your workday feeling overwhelming? Are you looking for ways to help streamline and optimize the way your team works? ChatGPT can make all this achievable – and it’s easy! All you need are templates. Templates are pre-defined text blocks that allow you to quickly construct messages or …

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The LEGO Bricks Analogy for Building AI Systems

Building with LEGO bricks is a fun and creative activity that allows you to construct various structures by combining different-sized bricks in multiple ways. This same concept applies to the development of artificial intelligence (AI) systems; you can connect different AI models to meet specific requirements, creating an impressive AI system like constructing a LEGO …

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Building a Competitive Advantage (Moat) for AI Start-ups in 2023

“Pre-trained model” is the name I will use to refer to AI models made accessible by companies like OpenAI, with capabilities including text, image, and embedding generation. You can contrast these pre-trained models with custom models trained to solve a specific task using “big data.” It’s 2023, and there are still growing start-ups with billion-dollar …

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Common Misconceptions about the Capabilities of ChatGPT & Large Language Models (LLM)

As an entrepreneur and consultant, I’ve spent years designing systems and implementing machine learning (ML) strategies using pre-trained models like those provided by OpenAI. During this time, I’ve talked to many business leaders seeking to improve their businesses with artificial intelligence (AI) and have encountered a few common misconceptions about what Large Language Models (LLMs), …

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Maximize Your AI Success: Embrace Flexibility and Agility

Are you looking to implement AI in your product or service but need help figuring out where to start? For example, are you deciding whether to fine-tune, prompt engineer, or train a custom model? It can be overwhelming with all the options and advancements in AI technology. However, maximizing flexibility and agility must be the …

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Alternatives to Fine-tuning

People commonly refer to fine-tuning when attempting to address their problems with AI. In a recent conversation with an OpenAI Ambassador, I was told that they “convince 90% not to use fine-tuning to solve their problem,” confirming my hunch that fine-tuning is one of the most misunderstood tools in AI. Fine-tuning is often more complicated and less …

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Introducing Smart Connections: The AI-powered Obsidian Plugin for Seeing Your Most Relevant Notes in Real-time

Are you tired of sifting through countless notes in your Obsidian vault to find the information you need? Introducing Smart Connections, the AI-powered plugin that revolutionizes how you organize and access your notes in Obsidian. As someone who has struggled with: I understand how frustrating it can be to lack the ability to work with your …

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Revolutionizing Search: How Hypothetical Document Embeddings (HyDE) Can Save Time and Increase Productivity

Tools like the OpenAI Embeddings API are changing the way search is performed. Instead of matching keywords, a language model converts text into vectors. These vectors represent the text using hundreds or even thousands of dimensions mathematically. This way, when searching, matches can be made based on the underlying intent of the query instead of just keywords. …

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Using ChatGPT to Write a 2,000 Word Article (Why I wouldn’t, but how I would do it)

While technically speaking, you cannot generate a 2,000-word article with ChatGPT, it can be done if you break the content into smaller pieces. In addition to the technical limitations, there are also style limitations to consider when generating content with ChatGPT. The style limitations are why I wouldn’t use ChatGPT. In this article, I will …

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Testing Verbose prompts in ChatGPT

An experiment based on prompts from the awesome ChatGPT prompts GitHub repo. Which performs better, asking a simple question to ChatGPT? Or prefacing the question with a verbose prompt? “Machine Learning” Test Question sourced from the repository: Full prompt from the repository: Test using just the question with ChatGPT: Test using the full prompt from the GitHub …

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