December 11, 2024 • 27 mins
My guest today is Tal Raviv, the genius behind the popular course entitled "Build Your Personal PM Productivity System & AI Copilot", which is a pretty fancy name. And it's also a pretty big deal for someone who previously described himself as an AI skeptic. But for Tal, the game changer was figuring out why so many PMs struggle to extract the full value out of their LLM tools and uncovering the tactics that actually have the power to transform your productivity.
And yes, we will be breaking those tactics down in this episode. We'll dive into how onboarding AI is much like onboarding a new team member, why iterative prompt engineering is key and practical advice for how to start tinkering with AI tools and build confidence in your skills.
Resources from this episode:
- Subscribe to The Product Manager newsletter
- Check out this episode’s sponsor: Wix Studio
- Connect with Tal on LinkedIn and Twitter/X
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Hannah Clark (00:01):
As I look back at all the topics we've
covered on the show thisyear, we have definitely
talked a lot about AI.
So I hope you don't mindif we throw one more on the
pile because this one isprobably the most actionable
AI episode we've done so far.
My guest today is Tal Raviv,the genius behind the popular
course entitled "Build YourPersonal PM Productivity
System & AI Copilot", whichis a pretty fancy name.
(00:24):
And it's also a prettybig deal for someone
who previously describedhimself as an AI skeptic.
But for Tal, the game changerwas figuring out why so many
PMs struggle to extract thefull value out of their LLM
tools and uncovering the tacticsthat actually have the power
to transform your productivity.
And yes, we will bebreaking those tactics
down in this episode.
We'll dive into how onboardingAI is much like onboarding
(00:45):
a new team member, whyiterative prompt engineering
is key and practical advicefor how to start tinkering
with AI tools and buildconfidence in your skills.
Let's jump in.
Welcome back to TheProduct Manager podcast.
I am here today with Tal Raviv.
And Tal, thank you somuch for joining us.
This is so exciting.
Can you tell us a little bitabout your background and how
you got to where you are today?
Tal Raviv (01:06):
Sure.
I started out studyingchemical engineering, which
has absolutely nothing todo with where I am today.
I mean, it does have inthe fact that it just
made me good at learninghard stuff and diving in.
I started a SaaS company witha few friends from college and
we did that for four years.
And then when it was timeto get a real job, it was
(01:27):
when like I was basicallylooking around, I was like I
really doing like support andmarketing and coding and design.
And what does the real jobversion of that look like?
And then there's thisnew thing called product
management at the time.
So I applied and I totallywouldn't be able to break
in today, but at the timeI was able to break in.
Hannah Clark (01:44):
Sweet.
Today, we're going to be talkingabout something else everyone
else is trying to break into,which is using AI effectively.
I think all of us are tryingto wrap our heads around how
can we use the technology moreeffectively, more practically.
So we're going to befocusing on how we can use
AI in our daily workflows.
But before we dig in, let'stake a second to talk a
little bit about what'sgoing on in your life.
So you are leading a cohortcourse to help PMs leverage
(02:06):
AI more effectively, kindof what was the journey
to lead you to that path?
Tal Raviv (02:10):
I started working on this course together with Maven
and I started working on thisone, we'll talk about this, but
I was at one hand working on alot of Riverside's generative AI
initiatives and leading those.
I totally wasn't using AI inmy day to day, I was like, Oh,
this is great for the product.
This has nothing todo with my day to day.
This can never help me with allthese things I need help with.
(02:31):
And so the course ended upbeing all about productivity
and building systems for yourproductivity for yourself, and
then your team as a productmanager really is very high
leverage for your productivity.
You're looking at yourorganization, managing emotions,
all this low tech stuff.
That's the course I teach.
And then in parallel, I was justplaying around a lot with AI.
(02:52):
The thing that kind of was theswitch that got me to actually
look at it as something thatI could personally use was I
was at Riverside and I just wasbetween a rock and a hard place.
And there was just no way Icould accomplish what I needed
to without leveraging ChatGPT.
And even then I kicked andscreamed and whined and then
my engineering team leadstill was like, let me just
(03:14):
show you just just stop.
This is how you do it, whichopened my eyes to this really
simple principle that thereason I wasn't using it right
was I wasn't giving it enoughcontext, just like a person.
And that was like amental shift for me.
And then I just startedlike pushing that
further and further.
I was like how muchcontext can I give it?
And what if I gave it thesame amount of context that I
(03:35):
would give a new hire that Iwas helping on board, right?
What if I had aconversation with it, like
I would with a new hire?
And then what if Istart to involve it in
a particular initiative,just like a new hire?
So I changed my mental model toreally think of it as what would
set a person up for success?
And I'm doing that in my sparetime and I'm teaching this
course, and then I started torealize, Oh my God, this could
(03:57):
actually be useful at work.
So it took me a long time,2024 for that to dawn on me,
and then over time I madethat part of the course.
So now the first two weeksof the course are about
getting your house in orderand all the things that still
really matter, even with AI.
And the final week isan intensive session of
three sessions of buildingyour PM AI copilot.
Hannah Clark (04:16):
Okay, awesome.
I'm excited to break intoeach of these things a little
bit on a more granular levelshortly, especially on board.
I think the idea of onboardingAI is super interesting.
I'm excited to get to that.
But before we get there,you described like your
own skepticism workingwith AI initially.
And I think this is justsomething common that we see
across the board in productmanagement and in other fields.
Why do you think that is?
(04:36):
Why are people skepticalor hesitant to sort of
bridge that gap betweenawareness and implementation?
Tal Raviv (04:42):
I think for most of the last, I think two years
since ChatGPT came out, twoand a half, it hasn't been that
great at a lot of stuff, or itwould require so much editing
and thinking that it was like,just like with the person you
hire or somebody really junioror why we don't hire an intern
for this particular role isit's just going to create
(05:03):
more work than it's worth.
And I think, first of all,what changed is that AI got
way better context windows gotbigger, personalities got better
and just like the models, theyjust got more intelligent.
And I think at this point, therewas a famous talk recently with
head of product of Anthropic andhead of product of OpenAI both
on stage and they said somethingto the extent of intelligence
(05:25):
is no longer the bottleneck.
There's ways to make itmore intelligent, but
it's not the bottleneck.
The bottleneck is eitherpre-training or contacts
or just what does it know?
What is it exposed to?
I think also if we show up toan LLM and we just have a one
sentence prompt, even if it'slike the biggest prompt expert
in the world swears by it andfine tuned it and crafted and
(05:47):
polished that prompt, it'sstill one sentence, right?
It's still going tobasically give you the
average of the internet.
And if you really wanted todo the kind of work that you
do, we are, any one of usas individuals is not the
average of the internet.
There's also knowledgethat we bring, we combine
and we synthesize.
So I mean, starting point,let's give it that knowledge.
See what it can do.
Hannah Clark (06:06):
Yeah.
I think this is areally good point.
I think sometimes we stepinto a chat and expect it
knows everything, so it shouldhave the perfect answer.
But it's if you stop someoneon the street while you're
just walking to the grocerystore and you're like, Hey,
can you spit out a PRD?
They're going to give youtheir best attempt without
all the context and ownedknowledge that you have.
So this is a, yeah, it'sone of those like dumb
moments that I don't thinkwe all really have had yet.
Tal Raviv (06:28):
I'll give another example that I think makes
this like really vivid.
This is when I talk to peoplethat don't happen to be in
product management, how Iexplain, I also use, I've
been experimenting withusing AI as my nutritionist
and keeping me on track.
And the way I explain it isif I was to work with a real
nutritionist, I wouldn't showup and be like, cool, tell
me what to eat and how muchand when and all that stuff.
(06:51):
Right?
Like the nutritionwould be like no.
How about you sit down andask you some questions?
I'm like no.
I just want you to tell me.
You suck as a nutritionist.
I'm out of here.
Right?
That wouldn't happen.
That's not a conversationthat would happen.
But somehow that's theconversation we have in AI.
Hannah Clark (07:02):
Yeah, that's spot on.
I hope it's working out for you.
I want to talk aboutthis AI Copilot.
This is all really interesting.
There's so many things,so many angles to approach
when we're talking aboutusing AI effectively.
So let's start with the tool.
So tell me about thistool, AI Copilot.
What's the game changer here?
Tal Raviv (07:18):
I'll say the components.
The components of AICopilot is Pick, ChatGPT,
Claude, something else.
I personally reallyClod for this.
Practically speaking,the projects feature,
it's a paid feature.
It's totally worth it.
It's just really good fromwhat I'm about to describe,
just makes it really easy.
These things justtake one click.
This can be accomplishedwith any LLM with a little
bit of duct tape and alittle bit of manual work.
(07:41):
So, first of all, you use custominstruction, system prompt,
whatever you want to callit, to tell it how to behave.
So you want it to be someonethat is challenging you, that
asks you lots of questions,that doesn't accept your
assumptions necessarily.
Who would be this idealcoworker that's sitting next
to you in like pair PMs.
And it would be somebodywho pushes you on certain
(08:04):
behaviors or values.
So, the behaviors I putfor myself is bias towards
action, try to givevalue to customers soon.
I try to make decisions withoutall the data, but that could
be totally opposite for otherindustries or companies.
And from there, so that's likethe personality that you hired,
think of an interview process.
That's the kind of personyou wanted to hire.
(08:24):
The next step is you hire themand you want to onboard them.
So if a new PM joined theteam, we'd probably give them
here's the that doc with themission, vision strategy.
Here's the deck with thecustomer persona, right?
And if it was a coach for me,I would probably say, here's my
performance review for the lastseveral performance reviews.
Here's what I'm struggling with.
(08:44):
Here's what my managergives me feedback on.
Here, again, going back to thenew PM, here's some gossip.
Hey, sit down, let'shave a cup of coffee.
Let me tell you about someof these stakeholders.
Let me tell you aboutsome of the people who are
going to be on your team.
Here's what they'reparticularly good at.
Here's where they needyou to lean in more.
Here's this stakeholder, and,what makes them feel good and
(09:04):
what stage of the process, butdon't step on this landmine.
And this just goeson and on, right?
Here's the org chart.
Here's my team.
This can be endless.
This would be what a newperson would be absorbing
from different sources,joining your company.
So you hire them, you've onboarded them, and then you've
got to put them to work.
You have to tell them, Hey,I need you to work on this
initiative, or I want you toguide me on this initiative.
(09:25):
So you start a thread andyou say here, I barely know
anything about this initiative.
I just got had this dumpedon me hallway conversation
with the founder.
We have to move fast on this.
Here's what I know.
Here's what I think.
And you have aconversation with it.
And then during that entirethread about that particular
initiative, you can startasking it for outputs.
It can be as simple as, theclassic example of writing
(09:46):
documents, fine, but itcan go way beyond that.
It can be a thought partner.
It can simulate ahard conversation.
What's the most importantthing you should do next?
The answers to thatare really amazing.
This is where the magic happensbecause it will constantly
reference actual names ofpeople in the organization.
It'll reference, Hey, this isan opportunity to really act on
(10:07):
that feedback that's constantlyin your performance reviews.
Hey, this is a really good timeto loop in this stakeholder.
And I've been blown awayby like the timing and what
I suggest those things.
It's very astute.
And during the initiative,I suggest gossiping
even more, right?
All the more context.
So, think of you're workingon something and, two weeks
into it, you have this hallwayconversation with, head of
(10:29):
sales and they just drop thislike constraint on you or your
manager says if it doesn't,we had this new information
and if it can't achieve this,if that's another scope, this
isn't worth shipping at all.
And you sit back at yourdesk and you're like, Oh,
my God, you won't believethe conversation I just had.
It could be whoever'ssitting next to you, do that
with your copilot, right?
They need to know that too.
And you can get all the I cango on and on about all the
things you can have it workwith you on and help you with.
(10:52):
But at the end of the day, it'salso worth closing the loop
and telling you what happened,sharing any retro results.
The magical moment is, oryou can ask it, okay, listen,
this is the end of the story.
It's great.
We shipped it.
It worked.
It didn't work.
Can you please tell me it'sall the new information that
you learned in this thread.
What should another newproduct manager know and
(11:14):
learn from this initiative?
And then you can, with Claude,one click, with ChatGPT, copy,
paste, add that back to thebrain, the project knowledge.
And, or use it inyour next thread.
That becomes all that contextthat you added this time.
It just got a little bit bigger.
For me, the reason I likeClaude, that's one click.
(11:36):
It's really optimized for that.
It immediately appliesto all your threads.
But that's the high level.
So that's how you get thiscopilot that gets smarter
over time, gets wiser,starts to apply lessons
from other initiativesright away and so on.
Hannah Clark (11:49):
I'm a little stunned right now.
I'm going to be honest with you.
This is a lot more comprehensiveand impressive than I expected.
So first of all, was thisthe conversation that you had
with your colleague before whosaid, no, let me show you, or
how did you put together thatthe tool was capable of that
kind of retention and decisionmaking capabilities and like
(12:10):
emotional intelligence ontop of all of the tactical?
I'm overwhelmed.
Tal Raviv (12:15):
I didn't know.
The way this connects for meit was like this light bulb
moment with my colleague earlyon where he's listen, that
example that started with, Hey,I really need to write a lot
of user stories really fast.
These drain my brain cells andI, it's a ton of time and they
have to be really precise.
And how the heck is ChatGPTgoing to help me with that?
I tried screenshotting Figma.
(12:36):
I tried Figma plugins.
It was like, this is morework than it's worth.
And then his suggestion was, howabout you sit down in ChatGPT,
give it a template, and thentell it everything that it needs
to know about this user story.
I was like, okay,that's a lot of typing.
He's no, don't type,dictate, just talk.
And another really importantdevelopment in the last
(12:58):
two years, year, isspeech-to-text got really good.
So OpenAI releasedthis Whisper model.
There's a lot of app developersthat implemented it on Mac
OS, on phone apps, and so on.
And if you compare thatwith like the native Apple
Windows dictation, it'sjust so much more accurate.
It makes me way moreexcited to use it.
It's way more useful.
(13:20):
I basically can just hold down abutton and just talk for a long
time, and it's super accurate.
So now the process starts tolook like I'm basically talking
the way I would when I havea new engineer joining an
initiative, or I'm kicking offan initiative, and ChatGPT is
very good at taking context,formatting in a certain way,
(13:44):
and inferring in between.
And so when I did that, I wasblown away because, first of
all, I'd never saw the designs.
It did things in theuser stories that I never
thought about, so itlike filled in the gaps.
And I almost haveto do no editing.
I might've if I had to deleteanything, it was basically
things that were just redundant.
It was just being overachiever.
That was like my lightbulb moment of Oh, context.
(14:05):
And then you add in the averageof the internet because,
products are not that unique.
Like it can figure out that,Oh, like the hallucinate
part is a good thing there.
It hallucinates goodstuff, connects the dots.
And once I was thinking inthat mode, I was just test,
I guess, kept pushing theboundary of like, how much
context can you get it?
Hannah Clark (14:23):
It's incredible.
Okay, so let's go intokind of like tactical
instructional mode here.
I'm sure that there's tonsof PMs listening who are
like salivating at theidea of using this tool.
So we've got our, LLM of choice.
If you're just wanting to getup and start, what are like the
critical steps that you wouldsay for onboarding the LLM?
Tal Raviv (14:41):
I would start simple.
If you're not paying, profor any of these services,
you should for otherreasons, but you don't have
to do it for this reason.
Start one thread and tellit how you want to behave.
I'd even say step zerofor all this is download
something like betterdictation for your computer.
The superwhisper, betterdictation, Wispr Flow.
There's a ton of these.
And practice withreally good dictation.
(15:04):
Everything else I say isgoing to be way easier.
Open a new thread, tellit how you want to behave.
This is a system prompt, puttingit at the top of the thread is
like 90 percent of the valueand then say, if you have a
doc, that's how the, if you cancopy paste the landing page of
your, you can talk about how youwould describe your product at
a cocktail party or something.
(15:25):
Even if you don't do all theorg chart stuff and you don't
talk about the stakeholders andyou don't give the performance
reviews, even just a littlebit, it's way better than what
you had before and you canpause there, just do that, just
do a little bit of context.
And then give a contexton an initiative that
you want to work on.
And that could be another 60seconds of, here's what I know.
(15:46):
So far, we know this signal.
We know we hear thesesupport tickets.
That's that.
And that's it.
Hit enter.
See what happens.
Hannah Clark (15:52):
Okay.
Have you, in your course, hadother folks follow this process?
Have you been able to see otherpeople generate value out of it?
I would love to hear likeanecdotes of how you've
seen this in action.
Tal Raviv (16:03):
We did a few beta groups and then started
to create a community.
And the community is likethe people who are really
pushing it forward, like reallyexperimenting, like way beyond
just one person like myself.
And I want to share, I justpulled it up right now.
And I just share likeeverybody's their aha moments.
So one person created reallyimpressive prototypes, not the
(16:24):
kind of prototypes that likewe've seen on Twitter demos,
like things that are like,wow, that's really precise
because I had so much context.
Some product managersjust use it to learn about
an industry that theywere like dropped into.
This is a not PM example, andI know somebody who used it
for quarterly planning, they'relike a product leader, which
is that's even further thanI've ever pushed in terms of
context that it would need.
A lot of people use itas a thought partner.
(16:45):
I think that's a reallygood mental model for this.
It's like just for conversationand understanding, and it helps
you think of better stuff.
And I think for writingPRDs, one person reported
here that what made thisparticularly a better way of
writing PRDs is it will askyou a lot of questions first.
And it was enough to knowwhat it doesn't know, and
it'll prompt you back tohave a conversation with
(17:06):
you and say, before I writethis PRD, I would really
want to know these things.
And you're like, Oh, of course.
Ask me anything.
Just write it for me.
It's amazing.
Yeah.
It's a reallyfascinating example.
It's people just like pushingit way beyond what I was doing.
Hannah Clark (17:16):
So incredible.
My head is already, I can feelthe gears turning in my mind
about how I can leverage this.
Cause it sounds like a lot ofthis, the general principles
here are cross applicable toa lot of different capacities.
Super cool.
Let's dive a little bit intoprompt engineering because
we're talking about givingcontext and like how important
it is to give good qualityinformation to the LLM and
to design your prompts well.
(17:38):
So what are some of the bestpractices that you've leveraged
strategies that people shouldadopt to create better prompts
and just be able to providebetter quality context?
Tal Raviv (17:45):
I think there was like a phase in LinkedIn where
like the entire feed was promptengineering slide decks and
like people changing theirtitles to prompt engineer.
And I remember just havingthis, like weird feeling
about them, like this can'tbe this way for a long.
This whole way of treatingprompts like magic spells
or magic potions orPokemon that you collect
it just doesn't feel right.
And lo and behold, overtime, those became less
(18:06):
and less important.
So when it comes to promptengineering, there's two things.
One, I've alreadytalked about context.
Treat it like a person,use dictation, makes
it a lot easier.
And the second thing is,just tell it what you want.
Just like a person beclear about what you
want, and then hit enter.
If it's not what you wanted,think why it didn't do what
you wanted, and go back to theprevious message and clarify it.
(18:29):
There's always like a,whatever tool you're using,
there's a little pencil onthat bubble in the chat.
And I think this is one ofthe most underrated features
of Claude or ChatGPT.
You can go back up and you canedit and add another sentence
or tell it, but I don't wantyou to approach it this way, or
be really clear about this, ormake sure that this is covered.
(18:49):
And it will just redo that.
It'll read you everythingthat's below that.
And that pencil, I think, isthe biggest tool for prompt
engineering in quotes, becauseat the end of the day, you're
just trying something, it'sa code, you try something
that it work, didn't work.
Let me change it.
Try it again and you don'thave to worry about, using
this special phrase or askingit to think a certain way.
(19:10):
Like, all these thingsare fading away.
They're quickly evaporating.
So it's not the right skill.
I don't think it'snot the right skill.
And the right skill istinkering, diving in and
just like iterating, justlike not being afraid of it.
It's all just a mindset change.
Hannah Clark (19:24):
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(19:45):
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Oh, okay.
I like that.
It's practical, but it'snot intuitive to just
think about, how can Ithink differently about how
I'm designing my prompts.
Okay, so I want to talk alittle bit about how you
(20:07):
have sort of approached this,because I think maybe something
we haven't been clear aboutthroughout this episode is
this is all sounding like it'sa tool that you're selling.
This is more of askillset than anything.
So why have you decided tofocus on this as a workshop
or offering it as a workshoprather than building a
scalable AI that alreadydoes all of these things?
(20:28):
Why did you feel thatwas the best approach?
Tal Raviv (20:31):
So as a career product person, coder,
I was very tempted.
Like my first impulse was,okay, that's an insight.
Like, how do I builda product here?
And when I just dug intowhat's missing, what's
stopping very smart people fromdoing this very smart thing?
What's in between?
It's not functionality.
It's not some UI or, some likenetwork effect or anything
(20:53):
that products can provide.
It's simply permission orguidance or a mindset shift.
So I thought I could build aproduct and then I could scratch
my head for the followingyear, trying to figure out
onboarding because everything Ijust described is a hell of an
onboarding for somebody to doon their own in their free time.
Nobody's going to do that.
Or I can be more valuableby being with people live,
(21:17):
answering questions, sayingthem, Hey, you cleared out
your schedule and now we'rehere on the Zoom together.
So I'm going to give you 5minutes to do this thing.
There's nothing, you've clearedout your schedule and I'm
here to answer your questions.
And basically buildit out together.
For me, that's like a90 something percent
activation rate if youwant talking product terms.
(21:37):
Right.
And I think that value retentionand all that, that just is
a way better approach to it.
So I think another reasonis also that a lot of the
things that are missing orthat could be better, I would
just be so shocked if thatwasn't on OpenAI or Claude.
There's so many things thatyou can tell would make
this process even better.
Like how they manage memory andchanges over time and do threads
(21:59):
know about each other and that'slike you were mentioning before.
So clearly applicable toall industries and all roles
that, I'd rather just helppeople use this correctly, the
barriers, mindset, behaviors,permission right now.
Hannah Clark (22:12):
That totally makes sense.
And it's just, yeah, I thinkbuilding an AI product, when
you know who you're up againstis, that's a, it's a big bet.
So I think that just leveragingthe tools that are available
more effectively, that's anarea I think a lot of people
really need to grow in.
And speaking of which, Ithink this is just a common
perception across the boardthat we're all sort of behind.
I think a lot of people knowthat they could be doing
(22:34):
more with AI, and there'ssome maybe an intimidation
factor about getting startedand really digging in.
So what would you say topeople to help them overcome
the perception and like reallystart building these skills in
a way that feels accessible?
Tal Raviv (22:46):
I was talking to somebody today, a
director of product, andshe said this really well.
She was like our entireorganization just missed the
boat on AI and productivityand applying in our roles.
And I told her, no, you didn't.
There is no boat.
Everybody feels that way.
I've had that conversationwith so many people.
These are people at thesecutting edge companies
that are in every otherway, the most modern ways
(23:08):
of running organizations.
They're all expressingsome kind of FOMO.
And I feel like Ihear the sentence.
I feel like other peopleare way ahead here.
I feel like somebody elseis doing this better.
So first of all, I can assureeverybody, that's not true.
This is something that everybodyright now is in the same place.
It feels that way becauseeverybody's talking about it.
Though, I think the, if you feellike that, just start tinkering.
(23:32):
Try it for something reallysmall and specific and keep
those principles in mind.
Context, right, and iteration.
Just set aside tenminutes to do that.
Build that tinkering muscle,and you'll see that snowballs.
It'll pull you in.
It'll just make youwant to iterate more.
And if that's the route youchoose, like very quickly,
you'll find yourself beingthe one teaching others.
So I think a lot ofit's it's intimidation.
(23:53):
It's feeling likeyou're really behind.
It's not even worth me trying.
If I try, I'm not goingto do it the right way.
Newsflash, everybodyfeels that way.
Hannah Clark (24:00):
I'm glad you said that because
I think that's true.
I think that there is thistendency of thinking like,
Oh the moment's passed.
It became big.
Everyone's ahead of us.
Why even try?
I bet.
When you think about it thatway, it's not a boat, it's a
taxi, it's a rideshare, it'sthere as a service for you,
it's at your disposal, youtell it exactly where to go.
Yeah, I like thatreframing as well.
I'm really appreciatingthe mindset work that we're
talking about today, Tal.
(24:21):
Anyway, so you'd mentioned afew different success stories
or kind of breakthroughs thatpeople had, either yourself
or someone else, what's theone that you think is like the
most, whoa, that you've seenworking with AI yourself or
working with other folks whoare learning how to use it?
Tal Raviv (24:34):
For me personally, my, Oh my God moment was
having pricing conversationsand it didn't, I have
to say, got it right.
It didn't just spit out theperfect answer, but it was just
a really good conversation.
And even when it got it wrong,it made me wonder why did
that feel wrong and say that.
The right word for thisis thought partner.
(24:54):
It was just like havinga smart person sitting
next to me, helping methink through something
and bouncing off ideas.
And then it helped meget to a better answer,
how to bundle something,how to price something.
And it's a reallysmart rubber duck.
It's like the nextlevel of rubber duck.
It's probably notwhat we imagined.
Like this would bethe aha moment for AI.
We probably imagined somethingvery sci-fi and like the
(25:17):
supercomputer from Hitchhiker'sGuide to the Galaxy.
But I think people ask me, doyou feel that it's wrong to
outsource your thinking to AI?
Do you feel that it's impacting,I think it's making me smarter,
just like if I had more smartpeople, more available to talk
to as much as possible, thatwould make me feel smarter.
Right.
That's what we all seek.
And I remember when we lookfor, where we work and who
(25:37):
we surround ourselves with.
So they, for me, pricing was areally cool aha moment that I
didn't think, do an amazing job.
But it's also a glow, like flowyou feel afterwards of I just
had a really good conversation.
Hannah Clark (25:48):
That's a really great example because it's
such a nuanced conversation.
We just had a really great chatwith Cem Kansu from Duolingo.
He's the head ofproduct at Duolingo.
It's a very complex conversationthat you have to have when
you're thinking about pricing.
And it's such a, you really haveto be so careful about where
exactly you place those markers.
So yeah, I can only imagineit's to your benefit to have an
incredible tool that's able tohelp you workshop your decision
(26:10):
making process and apply thatin future situations as well.
This is so fascinating, Tal.
Thank you so muchfor joining us.
I have reallylearned a lot today.
Where can people catch upwith you online if they
are curious about takingyour course or just want to
hear more of your thoughts?
Tal Raviv (26:23):
Sure.
LinkedIn is the first answer.
There's the course, it'son Maven for people who
are listening to this andthey're just like, I just
want to run ahead withthis and just tinker away.
On Maven I also I'm puttingthis like the demo I did.
You can just seeeverything I did.
I use this Notion playbookwhere it has all the
prompts and the steps andit's much more structured.
Basically, everything is inthis conversation just laid out.
That's something you canjust purchase without having
(26:45):
to take the whole course.
The course is just reallygood for people who want to
have access, open Q&A, officehours, reach me and like
just do much more together.
Hannah Clark (26:53):
Awesome.
Thank you so muchfor joining us.
It has been anabsolute pleasure.
Tal Raviv (26:57):
Thank you.
Hannah Clark (27:00):
Thanks for listening in.
For more great insights, how-toguides, and tool reviews,
subscribe to our newsletter attheproductmanager.com/subscribe.
You can hear more conversationslike this by subscribing to
The Product Manager, whereveryou get your podcasts.
As I look back at all the topics we've
covered on the show thisyear, we have definitely
talked a lot about AI.
So I hope you don't mindif we throw one more on the
pile because this one isprobably the most actionable
AI episode we've done so far.
My guest today is Tal Raviv,the genius behind the popular
course entitled "Build YourPersonal PM Productivity
System & AI Copilot", whichis a pretty fancy name.
(00:24):
And it's also a prettybig deal for someone
who previously describedhimself as an AI skeptic.
But for Tal, the game changerwas figuring out why so many
PMs struggle to extract thefull value out of their LLM
tools and uncovering the tacticsthat actually have the power
to transform your productivity.
And yes, we will bebreaking those tactics
down in this episode.
We'll dive into how onboardingAI is much like onboarding
(00:45):
a new team member, whyiterative prompt engineering
is key and practical advicefor how to start tinkering
with AI tools and buildconfidence in your skills.
Let's jump in.
Welcome back to TheProduct Manager podcast.
I am here today with Tal Raviv.
And Tal, thank you somuch for joining us.
This is so exciting.
Can you tell us a little bitabout your background and how
you got to where you are today?
Tal Raviv (01:06):
Sure.
I started out studyingchemical engineering, which
has absolutely nothing todo with where I am today.
I mean, it does have inthe fact that it just
made me good at learninghard stuff and diving in.
I started a SaaS company witha few friends from college and
we did that for four years.
And then when it was timeto get a real job, it was
(01:27):
when like I was basicallylooking around, I was like I
really doing like support andmarketing and coding and design.
And what does the real jobversion of that look like?
And then there's thisnew thing called product
management at the time.
So I applied and I totallywouldn't be able to break
in today, but at the timeI was able to break in.
Hannah Clark (01:44):
Sweet.
Today, we're going to be talkingabout something else everyone
else is trying to break into,which is using AI effectively.
I think all of us are tryingto wrap our heads around how
can we use the technology moreeffectively, more practically.
So we're going to befocusing on how we can use
AI in our daily workflows.
But before we dig in, let'stake a second to talk a
little bit about what'sgoing on in your life.
So you are leading a cohortcourse to help PMs leverage
(02:06):
AI more effectively, kindof what was the journey
to lead you to that path?
Tal Raviv (02:10):
I started working on this course together with Maven
and I started working on thisone, we'll talk about this, but
I was at one hand working on alot of Riverside's generative AI
initiatives and leading those.
I totally wasn't using AI inmy day to day, I was like, Oh,
this is great for the product.
This has nothing todo with my day to day.
This can never help me with allthese things I need help with.
(02:31):
And so the course ended upbeing all about productivity
and building systems for yourproductivity for yourself, and
then your team as a productmanager really is very high
leverage for your productivity.
You're looking at yourorganization, managing emotions,
all this low tech stuff.
That's the course I teach.
And then in parallel, I was justplaying around a lot with AI.
(02:52):
The thing that kind of was theswitch that got me to actually
look at it as something thatI could personally use was I
was at Riverside and I just wasbetween a rock and a hard place.
And there was just no way Icould accomplish what I needed
to without leveraging ChatGPT.
And even then I kicked andscreamed and whined and then
my engineering team leadstill was like, let me just
(03:14):
show you just just stop.
This is how you do it, whichopened my eyes to this really
simple principle that thereason I wasn't using it right
was I wasn't giving it enoughcontext, just like a person.
And that was like amental shift for me.
And then I just startedlike pushing that
further and further.
I was like how muchcontext can I give it?
And what if I gave it thesame amount of context that I
(03:35):
would give a new hire that Iwas helping on board, right?
What if I had aconversation with it, like
I would with a new hire?
And then what if Istart to involve it in
a particular initiative,just like a new hire?
So I changed my mental model toreally think of it as what would
set a person up for success?
And I'm doing that in my sparetime and I'm teaching this
course, and then I started torealize, Oh my God, this could
(03:57):
actually be useful at work.
So it took me a long time,2024 for that to dawn on me,
and then over time I madethat part of the course.
So now the first two weeksof the course are about
getting your house in orderand all the things that still
really matter, even with AI.
And the final week isan intensive session of
three sessions of buildingyour PM AI copilot.
Hannah Clark (04:16):
Okay, awesome.
I'm excited to break intoeach of these things a little
bit on a more granular levelshortly, especially on board.
I think the idea of onboardingAI is super interesting.
I'm excited to get to that.
But before we get there,you described like your
own skepticism workingwith AI initially.
And I think this is justsomething common that we see
across the board in productmanagement and in other fields.
Why do you think that is?
(04:36):
Why are people skepticalor hesitant to sort of
bridge that gap betweenawareness and implementation?
Tal Raviv (04:42):
I think for most of the last, I think two years
since ChatGPT came out, twoand a half, it hasn't been that
great at a lot of stuff, or itwould require so much editing
and thinking that it was like,just like with the person you
hire or somebody really junioror why we don't hire an intern
for this particular role isit's just going to create
(05:03):
more work than it's worth.
And I think, first of all,what changed is that AI got
way better context windows gotbigger, personalities got better
and just like the models, theyjust got more intelligent.
And I think at this point, therewas a famous talk recently with
head of product of Anthropic andhead of product of OpenAI both
on stage and they said somethingto the extent of intelligence
(05:25):
is no longer the bottleneck.
There's ways to make itmore intelligent, but
it's not the bottleneck.
The bottleneck is eitherpre-training or contacts
or just what does it know?
What is it exposed to?
I think also if we show up toan LLM and we just have a one
sentence prompt, even if it'slike the biggest prompt expert
in the world swears by it andfine tuned it and crafted and
(05:47):
polished that prompt, it'sstill one sentence, right?
It's still going tobasically give you the
average of the internet.
And if you really wanted todo the kind of work that you
do, we are, any one of usas individuals is not the
average of the internet.
There's also knowledgethat we bring, we combine
and we synthesize.
So I mean, starting point,let's give it that knowledge.
See what it can do.
Hannah Clark (06:06):
Yeah.
I think this is areally good point.
I think sometimes we stepinto a chat and expect it
knows everything, so it shouldhave the perfect answer.
But it's if you stop someoneon the street while you're
just walking to the grocerystore and you're like, Hey,
can you spit out a PRD?
They're going to give youtheir best attempt without
all the context and ownedknowledge that you have.
So this is a, yeah, it'sone of those like dumb
moments that I don't thinkwe all really have had yet.
Tal Raviv (06:28):
I'll give another example that I think makes
this like really vivid.
This is when I talk to peoplethat don't happen to be in
product management, how Iexplain, I also use, I've
been experimenting withusing AI as my nutritionist
and keeping me on track.
And the way I explain it isif I was to work with a real
nutritionist, I wouldn't showup and be like, cool, tell
me what to eat and how muchand when and all that stuff.
(06:51):
Right?
Like the nutritionwould be like no.
How about you sit down andask you some questions?
I'm like no.
I just want you to tell me.
You suck as a nutritionist.
I'm out of here.
Right?
That wouldn't happen.
That's not a conversationthat would happen.
But somehow that's theconversation we have in AI.
Hannah Clark (07:02):
Yeah, that's spot on.
I hope it's working out for you.
I want to talk aboutthis AI Copilot.
This is all really interesting.
There's so many things,so many angles to approach
when we're talking aboutusing AI effectively.
So let's start with the tool.
So tell me about thistool, AI Copilot.
What's the game changer here?
Tal Raviv (07:18):
I'll say the components.
The components of AICopilot is Pick, ChatGPT,
Claude, something else.
I personally reallyClod for this.
Practically speaking,the projects feature,
it's a paid feature.
It's totally worth it.
It's just really good fromwhat I'm about to describe,
just makes it really easy.
These things justtake one click.
This can be accomplishedwith any LLM with a little
bit of duct tape and alittle bit of manual work.
(07:41):
So, first of all, you use custominstruction, system prompt,
whatever you want to callit, to tell it how to behave.
So you want it to be someonethat is challenging you, that
asks you lots of questions,that doesn't accept your
assumptions necessarily.
Who would be this idealcoworker that's sitting next
to you in like pair PMs.
And it would be somebodywho pushes you on certain
(08:04):
behaviors or values.
So, the behaviors I putfor myself is bias towards
action, try to givevalue to customers soon.
I try to make decisions withoutall the data, but that could
be totally opposite for otherindustries or companies.
And from there, so that's likethe personality that you hired,
think of an interview process.
That's the kind of personyou wanted to hire.
(08:24):
The next step is you hire themand you want to onboard them.
So if a new PM joined theteam, we'd probably give them
here's the that doc with themission, vision strategy.
Here's the deck with thecustomer persona, right?
And if it was a coach for me,I would probably say, here's my
performance review for the lastseveral performance reviews.
Here's what I'm struggling with.
(08:44):
Here's what my managergives me feedback on.
Here, again, going back to thenew PM, here's some gossip.
Hey, sit down, let'shave a cup of coffee.
Let me tell you about someof these stakeholders.
Let me tell you aboutsome of the people who are
going to be on your team.
Here's what they'reparticularly good at.
Here's where they needyou to lean in more.
Here's this stakeholder, and,what makes them feel good and
(09:04):
what stage of the process, butdon't step on this landmine.
And this just goeson and on, right?
Here's the org chart.
Here's my team.
This can be endless.
This would be what a newperson would be absorbing
from different sources,joining your company.
So you hire them, you've onboarded them, and then you've
got to put them to work.
You have to tell them, Hey,I need you to work on this
initiative, or I want you toguide me on this initiative.
(09:25):
So you start a thread andyou say here, I barely know
anything about this initiative.
I just got had this dumpedon me hallway conversation
with the founder.
We have to move fast on this.
Here's what I know.
Here's what I think.
And you have aconversation with it.
And then during that entirethread about that particular
initiative, you can startasking it for outputs.
It can be as simple as, theclassic example of writing
(09:46):
documents, fine, but itcan go way beyond that.
It can be a thought partner.
It can simulate ahard conversation.
What's the most importantthing you should do next?
The answers to thatare really amazing.
This is where the magic happensbecause it will constantly
reference actual names ofpeople in the organization.
It'll reference, Hey, this isan opportunity to really act on
(10:07):
that feedback that's constantlyin your performance reviews.
Hey, this is a really good timeto loop in this stakeholder.
And I've been blown awayby like the timing and what
I suggest those things.
It's very astute.
And during the initiative,I suggest gossiping
even more, right?
All the more context.
So, think of you're workingon something and, two weeks
into it, you have this hallwayconversation with, head of
(10:29):
sales and they just drop thislike constraint on you or your
manager says if it doesn't,we had this new information
and if it can't achieve this,if that's another scope, this
isn't worth shipping at all.
And you sit back at yourdesk and you're like, Oh,
my God, you won't believethe conversation I just had.
It could be whoever'ssitting next to you, do that
with your copilot, right?
They need to know that too.
And you can get all the I cango on and on about all the
things you can have it workwith you on and help you with.
(10:52):
But at the end of the day, it'salso worth closing the loop
and telling you what happened,sharing any retro results.
The magical moment is, oryou can ask it, okay, listen,
this is the end of the story.
It's great.
We shipped it.
It worked.
It didn't work.
Can you please tell me it'sall the new information that
you learned in this thread.
What should another newproduct manager know and
(11:14):
learn from this initiative?
And then you can, with Claude,one click, with ChatGPT, copy,
paste, add that back to thebrain, the project knowledge.
And, or use it inyour next thread.
That becomes all that contextthat you added this time.
It just got a little bit bigger.
For me, the reason I likeClaude, that's one click.
(11:36):
It's really optimized for that.
It immediately appliesto all your threads.
But that's the high level.
So that's how you get thiscopilot that gets smarter
over time, gets wiser,starts to apply lessons
from other initiativesright away and so on.
Hannah Clark (11:49):
I'm a little stunned right now.
I'm going to be honest with you.
This is a lot more comprehensiveand impressive than I expected.
So first of all, was thisthe conversation that you had
with your colleague before whosaid, no, let me show you, or
how did you put together thatthe tool was capable of that
kind of retention and decisionmaking capabilities and like
(12:10):
emotional intelligence ontop of all of the tactical?
I'm overwhelmed.
Tal Raviv (12:15):
I didn't know.
The way this connects for meit was like this light bulb
moment with my colleague earlyon where he's listen, that
example that started with, Hey,I really need to write a lot
of user stories really fast.
These drain my brain cells andI, it's a ton of time and they
have to be really precise.
And how the heck is ChatGPTgoing to help me with that?
I tried screenshotting Figma.
(12:36):
I tried Figma plugins.
It was like, this is morework than it's worth.
And then his suggestion was, howabout you sit down in ChatGPT,
give it a template, and thentell it everything that it needs
to know about this user story.
I was like, okay,that's a lot of typing.
He's no, don't type,dictate, just talk.
And another really importantdevelopment in the last
(12:58):
two years, year, isspeech-to-text got really good.
So OpenAI releasedthis Whisper model.
There's a lot of app developersthat implemented it on Mac
OS, on phone apps, and so on.
And if you compare thatwith like the native Apple
Windows dictation, it'sjust so much more accurate.
It makes me way moreexcited to use it.
It's way more useful.
(13:20):
I basically can just hold down abutton and just talk for a long
time, and it's super accurate.
So now the process starts tolook like I'm basically talking
the way I would when I havea new engineer joining an
initiative, or I'm kicking offan initiative, and ChatGPT is
very good at taking context,formatting in a certain way,
(13:44):
and inferring in between.
And so when I did that, I wasblown away because, first of
all, I'd never saw the designs.
It did things in theuser stories that I never
thought about, so itlike filled in the gaps.
And I almost haveto do no editing.
I might've if I had to deleteanything, it was basically
things that were just redundant.
It was just being overachiever.
That was like my lightbulb moment of Oh, context.
(14:05):
And then you add in the averageof the internet because,
products are not that unique.
Like it can figure out that,Oh, like the hallucinate
part is a good thing there.
It hallucinates goodstuff, connects the dots.
And once I was thinking inthat mode, I was just test,
I guess, kept pushing theboundary of like, how much
context can you get it?
Hannah Clark (14:23):
It's incredible.
Okay, so let's go intokind of like tactical
instructional mode here.
I'm sure that there's tonsof PMs listening who are
like salivating at theidea of using this tool.
So we've got our, LLM of choice.
If you're just wanting to getup and start, what are like the
critical steps that you wouldsay for onboarding the LLM?
Tal Raviv (14:41):
I would start simple.
If you're not paying, profor any of these services,
you should for otherreasons, but you don't have
to do it for this reason.
Start one thread and tellit how you want to behave.
I'd even say step zerofor all this is download
something like betterdictation for your computer.
The superwhisper, betterdictation, Wispr Flow.
There's a ton of these.
And practice withreally good dictation.
(15:04):
Everything else I say isgoing to be way easier.
Open a new thread, tellit how you want to behave.
This is a system prompt, puttingit at the top of the thread is
like 90 percent of the valueand then say, if you have a
doc, that's how the, if you cancopy paste the landing page of
your, you can talk about how youwould describe your product at
a cocktail party or something.
(15:25):
Even if you don't do all theorg chart stuff and you don't
talk about the stakeholders andyou don't give the performance
reviews, even just a littlebit, it's way better than what
you had before and you canpause there, just do that, just
do a little bit of context.
And then give a contexton an initiative that
you want to work on.
And that could be another 60seconds of, here's what I know.
(15:46):
So far, we know this signal.
We know we hear thesesupport tickets.
That's that.
And that's it.
Hit enter.
See what happens.
Hannah Clark (15:52):
Okay.
Have you, in your course, hadother folks follow this process?
Have you been able to see otherpeople generate value out of it?
I would love to hear likeanecdotes of how you've
seen this in action.
Tal Raviv (16:03):
We did a few beta groups and then started
to create a community.
And the community is likethe people who are really
pushing it forward, like reallyexperimenting, like way beyond
just one person like myself.
And I want to share, I justpulled it up right now.
And I just share likeeverybody's their aha moments.
So one person created reallyimpressive prototypes, not the
(16:24):
kind of prototypes that likewe've seen on Twitter demos,
like things that are like,wow, that's really precise
because I had so much context.
Some product managersjust use it to learn about
an industry that theywere like dropped into.
This is a not PM example, andI know somebody who used it
for quarterly planning, they'relike a product leader, which
is that's even further thanI've ever pushed in terms of
context that it would need.
A lot of people use itas a thought partner.
(16:45):
I think that's a reallygood mental model for this.
It's like just for conversationand understanding, and it helps
you think of better stuff.
And I think for writingPRDs, one person reported
here that what made thisparticularly a better way of
writing PRDs is it will askyou a lot of questions first.
And it was enough to knowwhat it doesn't know, and
it'll prompt you back tohave a conversation with
(17:06):
you and say, before I writethis PRD, I would really
want to know these things.
And you're like, Oh, of course.
Ask me anything.
Just write it for me.
It's amazing.
Yeah.
It's a reallyfascinating example.
It's people just like pushingit way beyond what I was doing.
Hannah Clark (17:16):
So incredible.
My head is already, I can feelthe gears turning in my mind
about how I can leverage this.
Cause it sounds like a lot ofthis, the general principles
here are cross applicable toa lot of different capacities.
Super cool.
Let's dive a little bit intoprompt engineering because
we're talking about givingcontext and like how important
it is to give good qualityinformation to the LLM and
to design your prompts well.
(17:38):
So what are some of the bestpractices that you've leveraged
strategies that people shouldadopt to create better prompts
and just be able to providebetter quality context?
Tal Raviv (17:45):
I think there was like a phase in LinkedIn where
like the entire feed was promptengineering slide decks and
like people changing theirtitles to prompt engineer.
And I remember just havingthis, like weird feeling
about them, like this can'tbe this way for a long.
This whole way of treatingprompts like magic spells
or magic potions orPokemon that you collect
it just doesn't feel right.
And lo and behold, overtime, those became less
(18:06):
and less important.
So when it comes to promptengineering, there's two things.
One, I've alreadytalked about context.
Treat it like a person,use dictation, makes
it a lot easier.
And the second thing is,just tell it what you want.
Just like a person beclear about what you
want, and then hit enter.
If it's not what you wanted,think why it didn't do what
you wanted, and go back to theprevious message and clarify it.
(18:29):
There's always like a,whatever tool you're using,
there's a little pencil onthat bubble in the chat.
And I think this is one ofthe most underrated features
of Claude or ChatGPT.
You can go back up and you canedit and add another sentence
or tell it, but I don't wantyou to approach it this way, or
be really clear about this, ormake sure that this is covered.
(18:49):
And it will just redo that.
It'll read you everythingthat's below that.
And that pencil, I think, isthe biggest tool for prompt
engineering in quotes, becauseat the end of the day, you're
just trying something, it'sa code, you try something
that it work, didn't work.
Let me change it.
Try it again and you don'thave to worry about, using
this special phrase or askingit to think a certain way.
(19:10):
Like, all these thingsare fading away.
They're quickly evaporating.
So it's not the right skill.
I don't think it'snot the right skill.
And the right skill istinkering, diving in and
just like iterating, justlike not being afraid of it.
It's all just a mindset change.
Hannah Clark (19:24):
Web designers, this one's for you.
I've got 30 seconds totell you about Wix Studio,
the web platform foragencies and enterprises.
So, here are four thingsyou can do in 30 seconds
or less on Studio.
Adapt your designs for everydevice with responsive AI.
Reuse assets like templates,widgets, sections, and design
libraries across sites andshare them with your team.
Add no code animationsand gradient backgrounds
(19:45):
right in the editor.
And export your designsfrom Sigma to Wix
Studio in just a click.
Time's up, but thelist keeps going.
Step into Wix Studioand see for yourself.
Oh, okay.
I like that.
It's practical, but it'snot intuitive to just
think about, how can Ithink differently about how
I'm designing my prompts.
Okay, so I want to talk alittle bit about how you
(20:07):
have sort of approached this,because I think maybe something
we haven't been clear aboutthroughout this episode is
this is all sounding like it'sa tool that you're selling.
This is more of askillset than anything.
So why have you decided tofocus on this as a workshop
or offering it as a workshoprather than building a
scalable AI that alreadydoes all of these things?
(20:28):
Why did you feel thatwas the best approach?
Tal Raviv (20:31):
So as a career product person, coder,
I was very tempted.
Like my first impulse was,okay, that's an insight.
Like, how do I builda product here?
And when I just dug intowhat's missing, what's
stopping very smart people fromdoing this very smart thing?
What's in between?
It's not functionality.
It's not some UI or, some likenetwork effect or anything
(20:53):
that products can provide.
It's simply permission orguidance or a mindset shift.
So I thought I could build aproduct and then I could scratch
my head for the followingyear, trying to figure out
onboarding because everything Ijust described is a hell of an
onboarding for somebody to doon their own in their free time.
Nobody's going to do that.
Or I can be more valuableby being with people live,
(21:17):
answering questions, sayingthem, Hey, you cleared out
your schedule and now we'rehere on the Zoom together.
So I'm going to give you 5minutes to do this thing.
There's nothing, you've clearedout your schedule and I'm
here to answer your questions.
And basically buildit out together.
For me, that's like a90 something percent
activation rate if youwant talking product terms.
(21:37):
Right.
And I think that value retentionand all that, that just is
a way better approach to it.
So I think another reasonis also that a lot of the
things that are missing orthat could be better, I would
just be so shocked if thatwasn't on OpenAI or Claude.
There's so many things thatyou can tell would make
this process even better.
Like how they manage memory andchanges over time and do threads
(21:59):
know about each other and that'slike you were mentioning before.
So clearly applicable toall industries and all roles
that, I'd rather just helppeople use this correctly, the
barriers, mindset, behaviors,permission right now.
Hannah Clark (22:12):
That totally makes sense.
And it's just, yeah, I thinkbuilding an AI product, when
you know who you're up againstis, that's a, it's a big bet.
So I think that just leveragingthe tools that are available
more effectively, that's anarea I think a lot of people
really need to grow in.
And speaking of which, Ithink this is just a common
perception across the boardthat we're all sort of behind.
I think a lot of people knowthat they could be doing
(22:34):
more with AI, and there'ssome maybe an intimidation
factor about getting startedand really digging in.
So what would you say topeople to help them overcome
the perception and like reallystart building these skills in
a way that feels accessible?
Tal Raviv (22:46):
I was talking to somebody today, a
director of product, andshe said this really well.
She was like our entireorganization just missed the
boat on AI and productivityand applying in our roles.
And I told her, no, you didn't.
There is no boat.
Everybody feels that way.
I've had that conversationwith so many people.
These are people at thesecutting edge companies
that are in every otherway, the most modern ways
(23:08):
of running organizations.
They're all expressingsome kind of FOMO.
And I feel like Ihear the sentence.
I feel like other peopleare way ahead here.
I feel like somebody elseis doing this better.
So first of all, I can assureeverybody, that's not true.
This is something that everybodyright now is in the same place.
It feels that way becauseeverybody's talking about it.
Though, I think the, if you feellike that, just start tinkering.
(23:32):
Try it for something reallysmall and specific and keep
those principles in mind.
Context, right, and iteration.
Just set aside tenminutes to do that.
Build that tinkering muscle,and you'll see that snowballs.
It'll pull you in.
It'll just make youwant to iterate more.
And if that's the route youchoose, like very quickly,
you'll find yourself beingthe one teaching others.
So I think a lot ofit's it's intimidation.
(23:53):
It's feeling likeyou're really behind.
It's not even worth me trying.
If I try, I'm not goingto do it the right way.
Newsflash, everybodyfeels that way.
Hannah Clark (24:00):
I'm glad you said that because
I think that's true.
I think that there is thistendency of thinking like,
Oh the moment's passed.
It became big.
Everyone's ahead of us.
Why even try?
I bet.
When you think about it thatway, it's not a boat, it's a
taxi, it's a rideshare, it'sthere as a service for you,
it's at your disposal, youtell it exactly where to go.
Yeah, I like thatreframing as well.
I'm really appreciatingthe mindset work that we're
talking about today, Tal.
(24:21):
Anyway, so you'd mentioned afew different success stories
or kind of breakthroughs thatpeople had, either yourself
or someone else, what's theone that you think is like the
most, whoa, that you've seenworking with AI yourself or
working with other folks whoare learning how to use it?
Tal Raviv (24:34):
For me personally, my, Oh my God moment was
having pricing conversationsand it didn't, I have
to say, got it right.
It didn't just spit out theperfect answer, but it was just
a really good conversation.
And even when it got it wrong,it made me wonder why did
that feel wrong and say that.
The right word for thisis thought partner.
(24:54):
It was just like havinga smart person sitting
next to me, helping methink through something
and bouncing off ideas.
And then it helped meget to a better answer,
how to bundle something,how to price something.
And it's a reallysmart rubber duck.
It's like the nextlevel of rubber duck.
It's probably notwhat we imagined.
Like this would bethe aha moment for AI.
We probably imagined somethingvery sci-fi and like the
(25:17):
supercomputer from Hitchhiker'sGuide to the Galaxy.
But I think people ask me, doyou feel that it's wrong to
outsource your thinking to AI?
Do you feel that it's impacting,I think it's making me smarter,
just like if I had more smartpeople, more available to talk
to as much as possible, thatwould make me feel smarter.
Right.
That's what we all seek.
And I remember when we lookfor, where we work and who
(25:37):
we surround ourselves with.
So they, for me, pricing was areally cool aha moment that I
didn't think, do an amazing job.
But it's also a glow, like flowyou feel afterwards of I just
had a really good conversation.
Hannah Clark (25:48):
That's a really great example because it's
such a nuanced conversation.
We just had a really great chatwith Cem Kansu from Duolingo.
He's the head ofproduct at Duolingo.
It's a very complex conversationthat you have to have when
you're thinking about pricing.
And it's such a, you really haveto be so careful about where
exactly you place those markers.
So yeah, I can only imagineit's to your benefit to have an
incredible tool that's able tohelp you workshop your decision
(26:10):
making process and apply thatin future situations as well.
This is so fascinating, Tal.
Thank you so muchfor joining us.
I have reallylearned a lot today.
Where can people catch upwith you online if they
are curious about takingyour course or just want to
hear more of your thoughts?
Tal Raviv (26:23):
Sure.
LinkedIn is the first answer.
There's the course, it'son Maven for people who
are listening to this andthey're just like, I just
want to run ahead withthis and just tinker away.
On Maven I also I'm puttingthis like the demo I did.
You can just seeeverything I did.
I use this Notion playbookwhere it has all the
prompts and the steps andit's much more structured.
Basically, everything is inthis conversation just laid out.
That's something you canjust purchase without having
(26:45):
to take the whole course.
The course is just reallygood for people who want to
have access, open Q&A, officehours, reach me and like
just do much more together.
Hannah Clark (26:53):
Awesome.
Thank you so muchfor joining us.
It has been anabsolute pleasure.
Tal Raviv (26:57):
Thank you.
Hannah Clark (27:00):
Thanks for listening in.
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