Most of us first learned ChatGPT as a conversation. Chat feels like it is asking: What can I answer for you today? That is useful. ChatGPT Work feels different. Work feels like it is asking: What can I take off your plate? You are not just asking for an answer. You are giving ChatGPT an assignment. You define the outcome. You give it context. You let it use connected apps where available. Then you review the result before anything happens. That last part matters. OpenAI currently describes Work as the place for longer, multi-step tasks and finished deliverables, and apps can connect ChatGPT to external tools, information, and actions depending on your plan, workspace, permissions, and settings. (OpenAI Help Center⁠) Start With a Safety Boundary When you are first learning ChatGPT Work, I recommend adding this line to your prompt: Review and prepare the work, but do not apply, submit, send, message, delete, archive, move, update, schedule, cancel, publish, or modify anything without my explicit approval. That may feel like overkill. It is not. Connected apps make ChatGPT Work more useful because the information may already live in your email, calendar, files, or other tools. But useful does not mean uncontrolled. My simple rule is: Analyze first. Recommend next. Act only after approval. Practice This: Job Search Review For the first practice, let’s use a job search review. This is a good starting point because it is useful, but still easy to inspect. We are not asking ChatGPT to apply for jobs. We are not asking it to message anyone. We are not asking it to upload anything. We are asking it to review what it can access, organize the opportunities, and prepare a recommendation for us. Depending on what you have connected, ChatGPT Work may be able to review things like: - Job alert emails - Recruiter messages - Saved job descriptions - Career notes - A resume or professional summary - Relevant files in connected storage

When AI Looks Almost Human, “Almost” Becomes the Problem. Have you ever watched an AI-generated person who looked realistic, but something still felt wrong? The face looked human. The voice sounded human. The movements were close to human. But your brain still whispered: That is not a real person. That uncomfortable reaction is known as the uncanny valley. 🌀 What Is the Uncanny Valley? As robots, avatars, and digital humans become more realistic, people generally respond to them more positively. But there is a point where something looks almost human without being convincing enough. At that point, our comfort level can suddenly drop. Small imperfections become strangely noticeable: - Eyes that do not move naturally - Facial expressions that arrive a fraction too late - Lips that do not perfectly match the words - Skin that looks too smooth - A voice that lacks natural emotion - Body movements that feel slightly mechanical A cartoon character does not bother us because we know it is not human. We are not expecting perfect realism. An AI avatar that looks 98 percent human creates a different expectation. Our brains become extremely sensitive to the missing 2 percent. ⚠️ Why This Matters for AI: AI-generated video, voices, virtual assistants, and digital employees are improving quickly. The goal, however, should not always be to make AI indistinguishable from a human. Sometimes a clearly artificial character feels more trustworthy than a digital person pretending to be real. This creates an important design question: - Should an AI experience try to look completely human, or should it be honest about being AI? There is no universal answer. It depends on the audience, purpose, and level of trust required. The uncanny valley reminds us that technical realism and human comfort are not the same thing. As AI becomes more humanlike, the smallest details may determine whether people feel connected, curious, uncomfortable, or deceived. Share in the comments:

In Part 4, we talked about dialing model capability up or down. 📌 Now let’s practice it. My shorthand is: Find the floor, then move back up one level. That means you reduce the reasoning or effort level until you can see the model starting to miss the mark. Then you move back up to the lowest setting that still produces the quality you need. That gives you a practical balance between: - Output quality - Speed - Cost - Available usage or capacity - The amount of correction you have to do yourself Practice This: - Choose an assignment you already understand well. - Do not use something completely unfamiliar. - You need to be able to recognize when the output gets weaker. Good practice assignments include: - A business proposal - A project plan - An app framework - A book chapter outline - A training lesson - A process improvement plan ✅Step 1: - Use the model map from Part 3 to choose your product and model. - Start with the model you believe is appropriate for the work. ✅Step 2: - Run the assignment using a relatively high reasoning or effort setting. - Your goal is to create the first strong version of the plan, framework, proposal, or structure. - Save the result. This becomes your quality baseline. ✅Step 3: Once the main thinking is complete, reduce the reasoning or effort level one step. For example: High reasoning → Medium reasoning Or: High effort → Medium effort If the work is now very clear, and the product allows it, you can test reducing it two levels. ✅Step 4: Ask the model to continue the work. For example: - Expand one section - Rewrite part of the proposal - Create a summary - Draft an email from the plan - Turn the framework into a checklist - Create social posts from the article - Refine the tone - Format the output for easier reading This is where the test begins. You are no longer asking the model to do the hardest thinking from scratch. You are asking it to continue from an established structure.

In Part 2, we looked at the range of model capability. Now let’s make it practical. After more than 100 hours of testing, I have developed a fairly simple approach. I choose the product, model, and reasoning setting based on the type of work I am doing (not cost - yet). 🧠 Heavy Logic, Planning and Structure, for work such as: - Business proposals - Strategic frameworks - Complex planning - Coding - Difficult analysis - Multi-step problem solving - Assignments where maintaining context is critical - Consistency of theme or storyline For these applications, I want stronger reasoning, even if it takes longer. Product: ChatGPT Model: GPT-5.6 Sol Setting: High reasoning and for my book editor skill, Pro. Product: Claude Model: Claude Opus 5 Setting: High effort, with extended thinking when needed For these assignments, creativity in the delivery is secondary. My priorities are logic, structure, context integrity, planning, and getting the framework right. I rarely go higher, but keep in mind, most of what I do is front office business execution: strategy, project management, process refinement, education and training, and Cowork automation. I use Fable 5 rarely. I am not primarily using Claude to build sophisticated production applications. ✍️ Writing and Everyday Communication, for: - Blog posts - Creative writing - Emails - Business communication - Social posts - Editing and rewriting I usually want a capable model that leaves a little more room for creativity and variation. Product: Claude Model: Claude Sonnet 5 Setting: Standard/default effort Product: ChatGPT Model: GPT-5.5 Instant Setting: Instant I still use GPT-5.5 for this type of work while it remains available to me. The Interesting Exception: Image Creation Image creation breaks my normal rule. Why? I need the LLM to understand: - What I am trying to communicate - The audience - Composition and hierarchy - Style and mood - The creative objective So I need reasoning. But I also want the system to have enough freedom to interpret the brief creatively.

As a member of AI Bits & Pieces, you get a front-row seat to watch me “fail forward” with AI. That means I want you to feel free to experiment, try new things, fail, and succeed on your journey. And if it helps, "I'll go first." This video features Herman Moore and I having fun at his new studio. It includes a segment called, “What Does Rogaine Have to Do with AI?” 🤣 In this conversation, Herman and I talk about getting started with AI without fear or anxiety. Nobody needs to pretend they have it all figured out. AI is changing fast, but that also means there is always an opportunity to jump in, learn, and find practical ways to use it in your business and everyday work. And yes, they also cover AI hair filters, Rogaine, and why some bugs may be worth leaving unfixed. Studio and Footage Information: This video features footage from a recorded conversation at @Herman Moore's new studio as we work through audio and video strategy for the launch of a podcast. About AI Bits & Pieces: AI Bits & Pieces is a free, lighthearted AI learning community designed to help people build confidence with artificial intelligence, understand the fundamentals, and discover practical ways to use AI in everyday life and work. Invite friends, family and co-workers to learn about AI from free. Just give them the link below: https://www.skool.com/ai-bits-and-pieces/about Learn more about Herman Moore 84: https://www.hermanmoore84.com