TetraMesa

Prompt Iteration for Consumers – Getting the Best from Generative AI

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There’s a somewhat well-known joke about a photographer that went to a fancy dinner party. The host who was also the chef said to the photographer-guest, “I love your wonderful pictures; you must have a fantastic camera.” After a great dinner, the photographer said to the host, “Dinner was great. You must have a fantastic stove!”

The moral is clear enough. Though our tools can make a difference, going from good to great isn’t usually about the tool, it’s the craftsperson. The question is which kind of user do you want to be? This article isn’t about AI builders using what’s referred to as “Prompt Engineering.” The focus here is for generative AI users who want to improve results without deep technical exploration. Let’s do a little background first to set some context, then bullet point some techniques to try out. (Skip down to the bullet point list if you don’t care for the background.)

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Using AI for Mediocrity at Speed

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I’m a huge fan of LLMs… but this will be some of the subtle dark side. This isn’t an attempt to show AI doing horrid things. No. My concern is more subtle. It’s about insidious degradation of thought; in spite of the awesome power of AI to also help us learn and produce new things. My concern is that AI, eventually feeding upon itself, will regress us to average faster than ever.

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Prompt Engineering: What It Is and Isn’t

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Is Prompt Engineering just an overly fancy way of saying, “Here’s a better search query?” Maybe it should just be called Search 2.0? True enough, the output of an AI large language model is more than just a bunch of results, but the query itself is still ‘just’ an instruction of sorts, right?

In some cases, yes, it’s essentially the same as a fancy query. But mostly not. There are obvious differences in the use cases for prompts using AI Large Language Models (LLMs) vs how keywords get used in traditional search engines. And for all their potential faults and risks, LLMs can provide stunning new capabilities across a variety of use cases. At the same time, there seems to be some overblown expectations as to what prompts can do. For example, at least in some places, a misunderstanding that prompt engineering can make models better. While it may be true that prompts and responses can be iteratively honed and fed back into the fine-tuning of models to actually make models better, for the most part, they’re not used this way. I’d like to try to clear this up because I think it’s important we understand how we can use our tools and where they’re limited. Just to be clear, I’m not talking about the handful of folks who really are evaluating prompt output to adjust models. (If you’re one of those folks, you’re ideally operating more at the data science kind of level of prompt engineering.) For our purposes here, I’m talking about the typical consumer or business use that seems to have some people believing prompt input alone changes how the models themselves work.

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Will AI Fraud Finally Push Blockchain Products To Solve a Real Problem?

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We may be reaching the point where fraud and fakes are becoming so prevalent that the costs and pain are overwhelming enough that we’re ready to take the effort to try new solutions. Blockchain technology might finally have a mass market use here. But we’re not quite there. Except for crypto most of the work has been in infrastructure. Consumer use cases are few. This might be a natural evolution. The progression from infrastructure to platforms to applications is a movie we’ve seen before. And consumers likely don’t see safety and security issues as the most fun to deal with. At a certain point though, the answer to “where do you want to take the pain” turns into “ok, we have to deal with this now.”

The challenge remains getting adoption when there’s serious effort. After all, isn’t it annoying enough to have to identify how many traffic lights are in a picture just to get to your own account somewhere? Well, dealing with today’s crop of blockchain identity solutions is a bit more of a hurdle. At least for now. You may be thinking, “But don’t we already have all manner of fraud solutions, from credit checks to heuristic rules and now even AI trust scores?” Yes we do. And how is all that working for us?

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Foundations for Effective Remote Workforce Management

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So why not force return to office? There’s clearly benefits to the traditional model, right? Well, it should be obvious at this point there’s a great many benefits for remote work and hybrid options – at least for roles where it’s possible – for both employees and employers. (See Remote Work Statistics & Trends.) Potentially reduced costs on both sides, better work/life balance for employees, wider range of talent selection, etc. (However, note that cost reduction might be less than originally thought; stipends for remote gear, travel costs for team meetings that might not otherwise have been necessary, etc. So cost issues will as always be highly situational.)

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Product Discovery – Part 2 – Moving from Concept to Execution

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Introduction

This is a follow up to Product Discovery – Part 1 – Beyond the Checklist where we looked at a story as a lesson before considering the more tactical get-it-done checklists.

Here is product discovery at the highest level, summed up in two bullet points.

  • Living in the Problem Space: Asking good questions.
  • Living in the Solution Space: Coming up with answers that are: 1) viable; with market demand via value propositions(s), and 2) feasible; technically and operationally producible within some defined financial criteria, as well as compliant with any regulatory issues.

That’s it! Everything that follows are the details.

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Product Discovery – Part 1 – Beyond the Checklist

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Some of the best products happened through one of two means; the first is the traditional market research, deep study and so on. The other is practically by accident; often the flash of inspiration entrepreneurial route. Everything else is somewhere in the middle. This series is about how we can maybe do better in either case.

I’m going to handle this topic in two parts. This first is just a story illustrating the value we can learn by going deep into the weeds and emphasizes the value of deep discovery; in this case for a B2B product. Why bother? Because I think otherwise we may be missing things. After that, in a separate article, we’ll run through the more basic discovery checklists.

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More Product Management Lessons from Fire/Rescue/EMS and Beyond

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Welcome to Part 2 in this series. When I started Part 1, I had no idea how quickly a whole lot of thoughts would flow regarding the cross-over utility of some ideas from sometimes time-pressured or higher-stakes fields into general product management.

Train How You Fight

Another great line from a fire instructor, “You can never train too hard for a job that can kill you.” This stresses the importance of realistic training that prepares firefighters for real-life scenarios. And train we do. Beyond initial training, we have weekly sessions, special classes, and more. In aviation there’s recurrent medical and flight checks, etc. Now, in corporate, there are hopefully less things that will kill you than in emergency response situations. But there are systems and equipment breakdowns. There are high-stakes situations. Are you prepared? Have you practiced scenarios? Or is the book up there on the shelf getting dusty? The problem with risk mitigation is you don’t make money from it. It’s value preserving, not value accretive. It’s a cost item, not a revenue item. Backup systems, security efforts are necessary, but they are on the cost side. Regardless, the only way to be really ready is training. Some say – with some validity – that’s what insurance is for; to buy away risk. And yet, is that responsible? Is it ethical? Maybe. It depends on your situation. You have to assess that. It’s not all gloom and doom though. There’s items on the plus side here. What about customer training / onboarding? Or sales? Whether it’s to mitigate bad outcomes or drive towards good ones, are you constantly learning? The better the training, the better the game is likely to go. Train how you fight.

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