As some of you know I have been working on a few modules for the classroom WELCOME TO THE WISE ARCHITECTS CORNER - THE LEGENDS · Clief Notes For a while now the sign on the door read 👷 Coming Soon 🚧 The First Rung Is Now Open Welcome. This is where we will demonstrate how AI works, where we build human-AI systems that last, and where the person at the keyboard stays the author of the whole thing. You do not need a technical background for any of it. You bring the curiosity, I bring the blueprints, and we build the understanding together. 🪜 Here is the climb This whole corner is one ladder, five rungs, and each rung raises what you can do with the machine: (1) Prompt engineering, talking to the model (2) Context engineering, giving it what it needs (3) Memory engineering, making it remember (4) Loop engineering, making it check its own work and then repeat (5) Graph engineering, running a whole system of them, with you in the chair You walk in asking "how do I even talk to this thing?" and you walk out running a system of them. That is the journey, and the top of this ladder is a system of machines, the bottom is one sentence, and the bottom is open today. 📚 What is open today The whole first rung, Prompt Engineering, all six posts: the Series 1 Overview, three classes, the payoff with its quick card, and my perspective on the rung everybody says is dead. 📆 A few things before you start One series a week. Rung two lands next week, rung three the week after, and this corner is going to grow fast, five weeks to the top. Follow these modules in order. You will find that each rung stands on the one below it, the second assumes you took the first, the third assumes you took the second, and the path only works if YOU climb from the bottom. Start at the map. The Start Here post shows you how every series in this corner runs and points you to the first door, the Series 1 Overview. Do not skip it (I know, you want the techniques, they are one door further and they are not going anywhere, the map is a short read, and it saves you a whole series of wondering where you are).

WELCOME TO THE WISE ARCHITECTS CORNER - THE LEGENDS · Clief Notes @Bas Rosario Has been BUSY building out so much content for you all (with a lot more to come) please show him some love and more importantly go check it out! More resources for you all CLICK HERE: WELCOME TO THE WISE ARCHITECTS CORNER - THE LEGENDS

What is Jev? A newly released decision model: you give it text and a set of options, and it tells you which option fits and how sure it is. And it does so extremely fast and cheaply. I did several tests. This is what I saw. In my own test it labelled 14,007 emails in 4 minutes for about $0.50. Running the same job through Sonnet would have cost about $42 and taken a lot longer. On a smaller run the answers were as good while being 80 times cheaper and fast enough you could use it practically in real time. Why does any of this matter? Let me explain in an example of a workflow that would crank on this: You have incoming email. Send it through a decision tree: - Is the email for support or finance? - Jev - If support, what product is it for? - Jev - If for a TV, is it about the recall, or which of 3 TVs? - Jev - If recall, send canned message - code - Depending on which TV, assign to X person - code - Search documents based on the question and draft a first response - LLM The expensive LLM no longer does all the decision tree work it would have in the past. Jev does that for about 1/80th of the cost. The LLM (Or Human) is at the end of the tree not spending time pointing it in the right direction. The recall is an example of adding a new or temporary category. With Jev that means writing one more sentence describing it. No retraining, no labelled examples. If it's a recall, send them where to go for it and no LLM is called at all. It's cheaper than it looks. Questions about the same email can all go in one call: support or finance, which product, recall or not. The whole top of the tree comes back in one round trip. Only the LLM step at the bottom has to wait. Where it needs care There will always be ambiguity in text. LLMs and Jev are susceptible to this. Jev returns a confidence score that you can choose how to interpret. Is 70% good enough to move to sending a response before a human looks at it? It's cheap enough to run on every email even when a human still reads them all. It sorts the pile and flags the unsure ones first.

Awhile back I helped @Mary Julia Cordero with some issues she was having. I did a writeup in the classroom about it: https://www.skool.com/cliefnotes/classroom/b5143824?md=06c0cb5b8f884a319cad846a2bdd098a I wanted to turn this into a video to help explain the tips as I think this is a lesson everyone new to using AI has to learn along the way. Mary asked me her question as a part of my back to basics series. If you want to ask a question and have me answer it like this, drop a comment here: https://www.skool.com/cliefnotes/new-classroom-series-back-to-basics?p=32285243

Hey, I'm 72 hours into the school community. I've been learning so much. The course has been amazing. I've already met some awesome people in here. I just want to give a general thank-you and some positive feedback saying: - Great product - Great community - Love what's going on - Learning a lot - So much value here - Appreciate everyone involved Keep cranking!