How to Combine AI Experts, Apps, and Libraries

TL;DR

Create new value by sequencing specialized AI experts, apps, and libraries, then applying clear rules to limit the combinations to useful possibilities. Strong context, specific tasks, iterative follow-up prompts, and expert review turn raw AI output into practical articles, schedules, analyses, and customer-ready communication while helping marketers compensate for gaps in skills or available time.

Transcript

81 ways to create new value in the world how to combine experts apps and libraries and even as I say that you might be wondering where does 81 ways come from and I'd like to share that with you because I counted them here's the math this is an excerpt from a long chat where I asked data scientists to take experts apps in libraries and combine them ... Read More

Key Insights

• The 81 possibilities are derived by combining experts, apps, and libraries in sequences, examining the underlying mathematics, and introducing rules that constrain an initially enormous number of permutations and combinations to a manageable maximum.
• Specialized AI experts can work sequentially, with data scientists first analyzing combinations and a marketing professor later reviewing the complete conversation, unifying the findings, simplifying them, and turning them into a short explanatory article.
• AI can compensate for skill gaps by providing mathematical analysis to marketers who lack those abilities or by saving time for people who already possess them. The intended result is a more complete marketer who can produce outputs across previously weak areas.
• Communication formatting is presented as a conversion tool because editing text for color, boldness, clarity, and visual impact can make a shared interaction easier to read and more suitable for presentation to a customer.
• The AI system is described as a prosthetic rather than merely a software platform because it extends a marketer's working capabilities. The goal is to manage a specialized team of AI experts that can support analysis, writing, planning, and other outputs.
• Consolidation is necessary for practical mastery because separately learned steps must eventually become one usable action. The teaching analogy suggests moving from five conscious steps to three and finally to one integrated behavior that can be applied naturally.
• Specific context improves AI scheduling because the system can account for a short work week, available working periods, required tasks, and meetings. Follow-up instructions allow the initial schedule to be refined as additional constraints are introduced.
• Detailed tasks tend to produce more productive outputs than broad goals because AI performance depends on the information supplied. Broad requests can still be useful, but they often invite further questioning and need to be broken into smaller, more actionable components.

Questions & Answers

Q: How were the 81 AI workflow combinations calculated?

The calculation began by asking data scientists to combine experts, apps, and libraries in sequences, reveal the underlying mathematics, and lay out the possible arrangements. The resulting permutations and combinations were so numerous that rules had to be introduced. After the request was narrowed to the maximum number of possibilities allowed by those rules, the process arrived at 81.

Q: How can specialized AI experts work together in one workflow?

Specialized experts can be arranged in sequence so that each handles a different part of the work. In the example, data scientists analyze the combinations of experts, apps, and libraries and expose the mathematics. A marketing professor then reads the entire prior conversation, unifies the findings, makes them easier to understand, and writes a short article explaining the approach and result.

Q: Why should rules be added when generating AI combinations?

Rules make a large combination problem manageable and relevant. Without constraints, combining experts, apps, and libraries produced an extremely large number of permutations and combinations. The transcript says the possibilities became so numerous that rules were necessary. Applying a rule narrowed the space and allowed the system to provide a direct maximum of 81 possibilities instead of an unusably broad result.

Q: How can AI help marketers overcome skill gaps?

AI can supply capabilities that a marketer lacks or does not have time to apply. The transcript contrasts people who enjoy spreadsheet-oriented work with those who prefer writing-oriented work. By directing specialized AI experts, marketers can request mathematical analysis, clearer writing, scheduling, or other outputs, allowing them to work effectively in areas where they previously felt weak or incomplete.

Q: How does formatting AI output improve communication?

Formatting can make an AI interaction easier to read and more suitable for a customer. The demonstrated editing features include deleting text, changing color, adding bold formatting, and using undo. The larger point is that visual organization is not merely decorative. Clear emphasis and improved readability can increase visual impact and may improve the conversion performance of shared communication.

Q: How should a marketer consolidate AI skills into a workflow?

A marketer should repeatedly combine separate learned actions until they function as one practical behavior. The transcript compares this process with learning a martial arts kick through several steps, then reducing those steps from five to three and finally to one. In AI work, consolidation means integrating previous lessons, checklists, planning, and basic platform use into a repeatable workflow.

Q: How can AI create an efficient short work-week schedule?

Provide the available working period, explain constraints such as travel, and list the tasks that must be completed. In the example, the user specifies the remainder of one day and all of Wednesday, then identifies mandatory work. The AI organizes morning, midday, afternoon, and late-afternoon periods. A follow-up request then adds meetings so the schedule reflects more of the real workload.

Q: Why do detailed AI prompts produce better task plans?

Detailed prompts give the AI enough context to create specific, actionable outputs. One participant used broad goals and found the response helpful, but recognized that smaller tasks could have produced a more productive schedule. Another supplied a short work-week constraint, exact available periods, and required tasks. That richer context supported a structured plan that could be refined further by adding meetings.

Summary & Key Takeaways

• The number 81 came from asking data scientists to combine experts, apps, and libraries in sequences, expose the underlying mathematics, and enumerate the possibilities. Because the initial calculation produced an impractically large set of combinations, additional rules constrained the problem until the maximum useful set was reduced to 81 possibilities.

• A marketing professor then reviewed the full data-science conversation, unified its ideas, simplified the explanation, and produced a short article describing the approach and reasoning. This sequence demonstrates how one specialized expert can generate or analyze material while another expert converts that work into clear communication for a different audience.

• Effective AI use depends on consolidation, context, specificity, and iteration. The transcript contrasts broad goals with smaller tasks, showing that detailed inputs can produce more productive outputs. A scheduling example begins with available days and mandatory work, generates a structured schedule, and then incorporates meetings through an additional follow-up request.