Thanks for answering my question about chatbots. I haven't focused on chatbots because they're not that good and most businesses prefer a humanlike chatbot or none at all. I agree the AI space is changing; could you make a video on building a humanlike chatbot that handles FAQs, appointment setting, etc.?
Thanks for the request—I'd love to make a video on building a humanlike chatbot. Right now, the value of an AI assistant feels small because I can do tasks like scheduling appointments myself in just a few seconds, whereas earlier it would have taken much more day-to-day effort. The question is when the speed gap becomes so tiny that it's worth using an AI, or when the value becomes so high that I'd need an AI secretary. For me, it doesn't make much sense yet because I type extremely fast—about 186 words per minute—thanks to years of RuneScape grinding where I had to type the same thing repeatedly without copy‑paste. I'm also very fluid with computers and know hotkeys, so doing things myself is often faster than telling an AI to do them, especially given the current lag and the few words needed to prompt the model. That said, the landscape is changing: soon we'll be able to chain actions, so a single prompt could trigger dozens of steps like looking up flights, comparing costs, and booking them via APIs. We're approaching that point, but the models aren't there yet, which is why I've held back on chatbot videos. I will definitely build a humanlike chatbot for tasks like this in the future. A community member, Mehmud from Maker School, already sells Vapy chatbots and showed how powerful simple prompts can be, so a Vapy‑focused video might be worthwhile as well.
I used to be negative about chatbots because the technology was unreliable and too flexible for real business use. However, over the last six months things have improved; chatbots can now set meetings at 90‑95% of a human’s quality, making them valuable for high‑volume lead nurturing and booking. I see chatbots as sitting on the flexibility‑reliability spectrum: traditional automation (API calls) is highly reliable but inflexible, while AI trades some reliability for much greater flexibility. Chatbots are even more flexible but less reliable, which can be frustrating because clients often expect you to change their output and think you have full control. In my experience, selling chatbots has been tricky due to that need for constant tweaking. That said, their reliability is improving daily. If you start now, don’t expect quick money, but as the chatbot wave grows, focus on building more reliable and robust chatbots to take full advantage.
I haven't done it because I find there's very little business utility—these tools tend to work only about 80% of the time and the response quality is pretty poor, so most of this functionality is below the level where businesses would pay for it. My channel is all about businesses, strategies, and systems that businesses will actually pay money for. That said, I could definitely show the simplest, straightforward way to get up and running with this on your own, whether you want it as a hobby project or see an opportunity to monetize it. Here’s how retrieval‑augmented generation works: you feed a bunch of text into a database; the database indexes the text—turning it into numbers, essentially a high‑dimensional vector representation. When you ask a question, the system searches that database for snippets of text similar to your query (like Googling over your own data), retrieves those matches, and inserts them into the prompt for a large language model. The model then answers based on both the retrieved data and its own built‑in intelligence. To illustrate, I drew a quick diagram: RAG stands for retrieval‑augmented generation. Retrieval is just Googling over your own dataset—user inputs a query, the query searches the database, we find however many matches we want, we insert those matches into the LLM’s prompt, and the LLM answers the question. There are many ways to implement this, but the simplest way I’ll cover is using the OpenAI Assistants playground. In the playground you define the assistant’s instructions, then under Tools you enable File Search, upload a text file (I used one of my blog posts, stripped of formatting), and the system automatically creates a vector store and handles the embedding and retrieval for you. As a demo, I asked the assistant, “What are three components to a great high‑ROI automation?” It returned: a clear deliverable, it solves a hot‑button pain point, and you can template most of it. I also showed how to define a “pressing problem” as anything that impacts your ability to generate revenue, and the assistant gave a relevant answer. The result is your own RAG‑enabled chatbot. There are various services—like Chatbase, Botpress, or others—that let you run assistants through their back ends, or they may have their own RAG implementations to charge you for. You can embed this onto your website or service in many ways. I don’t sell it to businesses because I don’t find it very valuable for them, but chatbots are growing in popularity and functionality, so there’s definitely potential there. Hopefully that answers your question—thanks for asking it!
Yeah, absolutely — it's just a matter of time. We're already getting to the point where you can have really good, seemingly customized conversations with them, just using GPT-4.5 (the new model that just dropped about a week before this recording, and it's very good). I have no doubt that the next generation of models will let you simply say "hey, this is the business, here's some stuff they do, do it" — it'll automatically pick up the tone of voice, automatically pick up what you want it to do, and even ask you a clarifying question when you initialize it, like "do you want me to book appointments? What do you want me to do?" A lot of this is going to get done for you. When that happens, your point of leverage changes — it stops being about actually installing and implementing a chatbot with a bunch of agent features doing function calling or tool calling, and becomes about knowing where the tools are and the simplest, easiest way to deploy them for the customer. So yeah, we'll get there pretty soon — probably not for at least another year, maybe six months to a year — so there's still a lot of value in knowing how to do it manually right now.
High ticket is going to be tough here, but there are some simple AI-enabled services you could sell to online coaches like myself. One is need determination: if they run big communities, have a big brand, or get lots of YouTube comments, you can quickly build systems that scan everything customers are asking about, unclear on, or explicitly asking for videos on, and turn that into content. It usually surfaces very interesting insights most people miss. You can also build an "ask a question" funnel into the coach's inbound flow, or just tell people at the end of content to leave a comment. I pick what content to do next almost entirely based on your comments. How do I know this works? Because that's what I do, and I built the system myself. You wouldn't believe how many brands ask me what my packaging and ideation flow is, and the answer is I usually start with questions my customers ask me. Beyond that, you can use AI to shorten the time it takes to reach out to someone interested in a high-ticket offer, say $1,000 to $5,000 a month with four or five months of average churn, which might be worth $20,000. The usual flow is ads, PPC or organic to a landing page with a book-a-call funnel, setters reaching out to book the appointment and maximize show rate, then the closer on the call. You can use AI to automate a large portion of that, cut speed to lead, improve the quality of the conversations through intelligent automated replies, and automate the follow-ups.