@Tom Nelson thanks, it was not an easy path to get to that realization. Once you realize it, your business changes and the quality of your clients changes.

@Nika Marsagischvili I walked through my initial explanation with Astra and came up with the following, which I think will help you develop your pricing strategy. Pain → Consequence → Desired Outcome → Value → Price For example, instead of asking, “What should we charge for this service?” start with, “What problem is the customer paying us to make go away?” Imagine a business owner has a problem costing them $100,000 a year. If your solution reliably reduces that loss by $60,000, charging $10,000–$20,000 may represent an excellent value proposition. The customer isn’t really buying 20 hours of your time. They’re buying a $60,000 improvement. The key concept is the value gap: Value created for customer − Price paid = Customer’s perceived value. So if: - Current pain costs the customer $50,000 - Your solution reduces that pain by $40,000 - You charge $10,000 - Customer receives roughly $30,000 of net economic value That feels dramatically different from saying, “Our service costs $500 an hour.” There are actually three kinds of pain worth identifying. Economic pain is money being lost or opportunities being missed. Operational pain is wasted time, complexity, inefficiency, staffing problems, or hassle. Emotional/risk pain is uncertainty, fear, frustration, exposure, or loss of control. The strongest offers often solve all three. A useful pricing conversation therefore starts with questions such as: “What happens if you don’t fix this? What is it costing you now? How much time does it consume? What risk does it create? What would solving it allow you to do? How valuable would that outcome be?” Then price the solution so there is an obvious disparity between what the customer pays and what the customer gets. The 10-to-1 test. You don’t literally need a 10× return every time, but the customer should be able to look at the proposition and think: “Giving you $X to make this $Y problem disappear is an easy decision.” One other important distinction: don’t price the activity; price the transformation.

The decisions and approaches made during a chat can sometimes be a very important knowledge base. I have been playing around with a concept adopted from the US Army Ranger Handbook called the After Action Report (AAR). Basically, it is four questions ( might be adding a few extra) that I have the AI ask and answer and produce a report for future reference. I am still working out the full flow, but I find that the immediate post session debrief has benefits in improving approaches and identifying decisions made during a specific session. So the session itself does not become a knowledge base, the AAR for the session does. Once I have the AAR skill ready for prime time I want to see if anyone else finds it useful.

@Tony Iverson I am working from these base questions that come from the actual ranger handbook: - What was planned? (Reviewing the stated intent, training objectives, or operational plan/orders). - What actually happened? (Establishing an objective timeline and common baseline of facts). - Why did it happen? (Analyzing the root causes of successes and discrepancies between the plan and reality). - What do we do differently next time? (Formulating forward-looking, actionable changes, sustaining strengths, and correcting weaknesses). Obviously, I am working on tweaking them to better fit an AI session, but these four base questions do return ( in my opinion) some quality results. It is a good starting point to build from.

Thank you for sharing this, as the graphic and discussion is exactly what I needed to clearly understand memory systems. Your cross post in another discussion got me here. Between the discussions in both posts the light bulb is flickering more!

@David Vogel thanks! I am finishing up ring two now so I can start on ring three!

@Dino Behler it took a tool that had some functional limits and made it perform outside of its normal parameters: I am working on some other experiments to see what else I can do with my legal ai Frankenstein!

@Gina Wang it took a good legal ai and made it much better. Never dawned on me that I could try this. Now I am playing with setting up folders differently within the system to try and leverage how content is used and improve the quality of the outputs

Not in Lyceum, but I felt compelled to comment. First, you cannot be this hard on yourself; negative thoughts create negative results. You are with a great group and an incredible teacher. Have faith in yourself and believe in the future you want to create. Manifest positive results starting right now! Questions you cannot answer now will not be ones you can't answer in the near future. You know the questions that concern you, now go out and own them, you got this!