Gina Voya (They/Them)

Opinions are mine & don't reflect employer's views.
Queer Coptic Alexandrian Asylee on Duwamish Land

[Andrew Ng @AndrewYNg]

AI Product Management

AI Product Management is evolving rapidly. The growth of generative AI and AI-based developer tools has created numerous opportunities to build AI applications. This is making it possible to build new kinds of things, which in turn is driving shifts in best practices in product management — the discipline of defining what to build to serve users — because what is possible to build has shifted. In this post, I’ll share some best practices I have noticed.

Use concrete examples to specify AI products. Starting with a concrete idea helps teams gain speed. If a product manager (PM) proposes to build “a chatbot to answer banking inquiries that relate to user accounts,” this is a vague specification that leaves much to the imagination. For instance, should the chatbot answer questions only about account balances or also about interest rates, processes for initiating a wire transfer, and so on? But if the PM writes out a number (say, between 10 and 50) of concrete examples of conversations they’d like a chatbot to execute, the scope of their proposal becomes much clearer. Just as a machine learning algorithm needs training examples to learn from, an AI product development team needs concrete examples of what we want an AI system to do. In other words, the data is your PRD (product requirements document)!

In a similar vein, if someone requests “a vision system to detect pedestrians outside our store,” it’s hard for a developer to understand the boundary conditions. Is the system expected to work at night? What is the range of permissible camera angles? Is it expected to detect pedestrians who appear in the image even though they’re 100m away? But if the PM collects a handful of pictures and annotates them with the desired output, the meaning of “detect pedestrians” becomes concrete. An engineer can assess if the specification is technically feasible and if so, build toward it. Initially, the data might be obtained via a one-off, scrappy process, such as the PM walking around taking pictures and annotating them. Eventually, the data mix will shift to real-word data collected by a system running in production.

Using examples (such as inputs and desired outputs) to specify a product has been helpful for many years, but the explosion of possible AI applications is creating a need for more product managers to learn this practice.

Assess technical feasibility of LLM-based applications by prompting. When a PM scopes out a potential AI application, whether the application can actually be built — that is, its technical feasibility — is a key criterion in deciding what to do next. For many ideas for LLM-based applications, it’s increasingly possible for a PM, who might not be a software engineer, to try prompting — or write just small amounts of code — to get an initial sense of feasibility.

For example, a PM may envision a new internal tool for routing emails from customers to the right department (such as customer service, sales, etc.). They can prompt an LLM to see if they can get it to select the right department based on an input email, and see if they can achieve high accuracy. If so, this gives engineering a great starting point from which to implement the tool. If not, the PM can falsify the idea themselves and perhaps improve the product idea much faster than if they had to rely on an engineer to build a prototype.

Often, testing feasibility requires a little more than prompting. For example, perhaps the LLM-based email system needs basic RAG capability to help it make decisions. Fortunately, the barrier to writing small amounts of code is now quite low, since AI can help by acting as a coding companion, as I describe in the course, “AI Python for Beginners.” This means that PMs can do much more technical feasibility testing, at least at a basic level, than was possible before.

Prototype and test even without engineers. User feedback to initial prototypes is also instrumental to shaping products. Fortunately, barriers to building prototypes rapidly are falling, and PMs themselves can move basic prototypes forward without needing professional software developers.

In addition to using LLMs to help write code for prototyping, tools like Replit, Vercel’s V0, Bolt, and Anthropic’s Artifacts (I’m a fan of all of these!) are making it easier for people without a coding background to build and experiment with simple prototypes. These tools are increasingly accessible to non-technical users, though I find that those who understand basic coding are able to use them much more effectively, so it’s still important to learn basic coding. (Interestingly, highly technical, experienced developers use them too!) Many members of my teams routinely use such tools to prototype, get user feedback, and iterate quickly.

AI is enabling a lot of new applications to be built, creating massive growth in demand for AI product managers who know how to scope out and help drive progress in building these products. AI product management existed before the rise of generative AI, but the increasing ease of building applications is creating greater demand for AI applications, and thus a lot of PMs are learning AI and these emerging best practices for building AI products. I find this discipline fascinating, and will keep on sharing best practices as they grow and evolve.

[Original text: https://t.co/ohLyrpU4SJ ]

[Gina Voya (They/Them) @GinaVoya]

@reg_inee I am speaking of tiktok specifically as an example, a sense of community and closeness/relatability that is hard to establish at this scale in real life + a hunger for information about new products, trends, FOMO.

[Gina Voya (They/Them) @GinaVoya]

https://t.co/SSDyFaFdzb

https://t.co/8H1BW8ewDq

[Gina Voya (They/Them) @GinaVoya]

📢UX enthusiasts of all levels in the Puget Sound/Seattle area📢
I have recently joined our local ACM SIGCHI chapter as program co-chair and our board needs two more members.
Time commitment between 6~10 hr/mo. Please reach out for more details!

[Puget Sound SIGCHI @pssigchi]

Hello UX community! 👋

We held our first board meeting of the season yesterday & we're excited to get started with new events! Keep your eyes out in the coming weeks for announcements! 🎉

We're looking to fill 2 open positions on the board: *Vice Chair & Secretary.* 1/3

[Warp @warpdotdev]

Warp is now in public beta!

Try it out now, and read the thread to learn more about our launch.

[Navalism @NavalismHQ]

"If the work doesn’t require creativity, delegate it, automate it, or leave it."

@naval

[Gina Voya (They/Them) @GinaVoya]

Outrageous.

[Gina Voya (They/Them) @GinaVoya]

@TROYUnews
#TroyUniversity

[Women Who Code @WomenWhoCode]

👩🏾‍💻 Ada Lovelace was the first computer programmer!
#programmer #developer

[Gina Voya (They/Them) @GinaVoya]

"My life’s work is about humanizing technology, before it dehumanizes us"
I can’t wait for #GirlDecoded to come out in 3 weeks and to learn more about #RanaElKaliouby's journey. Check it out on https://t.co/QSogiHXkd4

#Affectiva #HCI #CS #StartUp #WomenInTech #Egyptian

[Gina Voya (They/Them) @GinaVoya]

@samoalfred Interested!

[BlaqBobby @Afr030B0TiCiST]

Look what these kids did with Arduino.
😍😍😍😍I absolutely love it!!!
Video credit:. Robotik Kodlama on insta.

[Gina Voya (They/Them) @GinaVoya]

😭✊🏼❤️

[Malala Yousafzai @Malala]

She’s the only friend I’d skip school for.

[Gina Voya (They/Them) @GinaVoya]

❤️✊🏼

[Greta Thunberg @GretaThunberg]

So... today I met my role model. What else can I say? @Malala

[Evan Peck @EvanMPeck]

Luke-warm take: CS college curriculum also has a bad habit of forgetting CS can be fun.

I whipped together a last-minute 80-min seminar last fall called 'The Joy of Programming' because a guest speaker couldn't make it.

It was probably the most popular talk I gave all semester

[julia ferraioli @juliaferraioli]

🎶 GitHub is a social media platform 🎶

[Gina Voya (They/Them) @GinaVoya]

@Skkyy__ انا مسحتهم already

[julia ferraioli @juliaferraioli]

Most of technology is held together with the digital equivalent of masking tape. Not duct tape. Masking tape.

[emily @emilyst]

Why is it called an "infinite loop" instead of "`while`-ing away the hours"?

[Dagmar Monett @dmonett]

Literally all I do as a 30+ years #AI expert:

No, that's not AI.
No.
Robots won't take over the world.
No.
That's 1 in many AI subfields, not AI.
Still they won't take all jobs.
No.
Nein.
Deep learning alone won't solve intelligence.
No.
Nope.
The AI hype is boring.
Define AI.