Pratica di Shadowing: What REALLY Happens in Meta’s AI Product Sense Round - Impara a parlare inglese con i video
Scegli una modalità di pratica
Ogni modalità apre una sala di pratica a schermo intero.
218 frasi
Se le frasi sono troppo corte o troppo lunghe, clicca su Edit per modificarle.
1
You've done interviews like this before.
2
What's different about this?
3
They have you use like Llama to actually vibe code a product in the AI product sense interview.
4
So like my first loop was a normal traditional product sense, a normal analytical.
5
And then there was an AI product sense after that, that was specific to using, they would give you like a normal product sense case,
6
and then you walk through it, use cases, segmentation solution.
7
And then they stop you at the solution
8
and tell you to move over to the meta vibe coding board and start sort of prototyping that solution.
9
And so I thought that was really unique.
10
And that was, at that time, that was really out of the ordinary for me.
11
And I had already practiced, like, you know, with Kershaw Cloud Code, I use a lot of those tools daily anyways.
12
But I'd never been asked that in an interview before.
13
And now it's definitely becoming kind of the norm, which is fun and cool, but also comes with its own challenges, you know.
14
In the AI product sense round, just start from the beginning and just kind of walk me through play-by-play yeah
15
so i joined the call um i was interviewing with another senior product manager
16
that worked on the team um and he said
17
that he's going to give me a prompt not really it's
18
going to sound like a normal traditional product sense prompt he wants me to go as far as the solution
19
and then stop
20
and use 30 minutes to flush out sort of my use cases my clarifying questions my user groups
21
and my um and which ones i prioritize for solution stop at the 30 minute mark
22
and then go over to uh he sent me a link to like a llama choppa chatbot um
23
and he wanted me to vibe code whatever solution i had in mind at
24
that point so it was like 30 minutes like sort of traditional product sense kind of
25
and then 30 minute prototyping what was the actual prompt prompt
26
was you're a product manager at meta you've been put in
27
charge of a brand new product for volunteering what would you do
28
and why so from there i kind of started with clarifying questions
29
I asked if there was any time or resource constraints when I think about solutioning.
30
He said, no. I said, will it be built as part of one of Meta's ecosystem of products or standalone?
31
He said, you decide.
32
And then I said, am I both mobile or web?
33
And he said, your decision.
34
We've built a database that houses all of the top companies
35
and their interview processes from anonymous reports so
36
that you can learn what really happens in the room in the interviews at Anthropic, OpenAI, and Fang+.
37
Check out our full library of interview experiences in the link below.
38
Submit your own whenever you have one of these
39
and pay it forward so that you can reap the same benefits from what you're getting from the community.
40
So the only real constraints were voluntary volunteers and unlimited time and resources, which is not really a constraint,
41
but kind of an unlimited constraint and everything else you decide.
42
And so they decide to have you use their LLAMA tool.
43
They bring you into their tool.
44
You use that tool.
45
What do you remember in your notes from how the interviewer reacted to how you used AI?
46
Specifically, were there moments where they like pushed back on your prompts or outputs?
47
So I went through goals.
48
I went through user groups.
49
I went through pain points and then solutions.
50
And so once I went over to the prompt, it was very like, I won't say cursor-esque because I think the cursor UX is much better.
51
I would even say the quad code UX is much better.
52
But it was very much like ask a bunch of questions.
53
I put in my whole prompt notes, like my assumptions, my pain points, and my user group, as well as my solution.
54
Once I put that in, it went to work for like, I want to say five to seven minutes.
55
And then from there, I started making iterations.
56
And he asked me questions around latency.
57
He also asked me questions around why I wouldn't leverage image generation.
58
And then he also asked me about what did I think about interference, compute, as well as retrieval.
59
So that was all those questions sort of came as follow ups after I had that first prototype up.
60
And I clicked through it and kind of showed him what I was thinking and what I wanted to change.
61
And he was asking me like, oh, is this the most efficient way to prompt it?
62
Like, aren't you using more tokens versus prompting it for this?
63
Like maybe, maybe like for a good example was I was trying to get to generate a chart of things
64
that the users apply to.
65
And he was asking me if I should let the LLM determine what chart to use.
66
And so I was prompting for that.
67
And he's like, oh, don't you think that'll eat more tokens?
68
Like maybe there'll be less latency if you use this type of chart.
69
And so there was follow up questions like that.
70
But it honestly kind of felt to me, even when I was interviewing with him, that it was his first time doing this as well, because there's a lot of times that I was typing things in,
71
or I was like, about to hit send.
72
And he was like, oh, let's rethink this.
73
And I was like, oh, okay, let's talk about it.
74
That's something that we're hearing.
75
And even like all across the rounds nowadays, all these Vibe Coding rounds are so new.
76
There's no question banks that people can rely on where they can just like, you know, so I totally hear you.
77
There's a real thing.
78
People are saying that.
79
I want to go back to these follow-up questions are super, super cool.
80
Did he ask you another question about latency besides the prompting it in a different way to use less tokens?
81
He asked me once I finished like all my follow-up prompts and like towards the end of the overall thing, he went back to latency again and was like,
82
what's a better way you could have maybe built this prototype with the consideration of CompuPower, token optimization, and latency in general.
83
What about your friend?
84
Did he give you any insights into maybe the question that he got asked?
85
Did he get the exact same prompt about volunteering?
86
His prompt was about if Meta wanted to create an app, a map app, how would you go about creating it?
87
And he also had 30 minutes to kind of do the traditional sense
88
and then bring it over to a live coding session
89
and he was asked mainly about retrieval like where is he getting all this data from
90
because they don't have a map api right now how would
91
you get all the various geographic data how would you get
92
coordinate how would you maybe built in like directional uh like features within the app within the map
93
and so his was a little different
94
and honestly it sounded like a lot of his follow-ups were
95
specifically on the actual product versus mine being very much about like
96
the LLM tooling and the tool calling and token optimization and latency.
97
Like, he didn't get any questions about that.
98
His was all based on, like, retrieval and, like, how he would build to call different APIs to retrieve data in a more efficient manner.
99
And yours asked about retrieval, but maybe only, like, once.
100
Yeah, dude.
101
Just, like, where would we retrieve all the—first of all, if we're pulling data from—for all these volunteering opportunities,
102
what's the best way to optimize the LLM for retrieval specifically on those?
103
And then secondly, if the user applies, what's the best way to actually know that we're sending them down the correct path?
104
What would you do differently if you were going to prepare for the round again?
105
I would probably, one, brush up really well on my technicals when it comes to just vibe coding in general.
106
Like optimizing for specific clarifying questions up front.
107
Like even the meta bot, like it didn't really like you know how like cursor
108
and cloud code
109
if you used it will ask you like a like a
110
multiple choice question almost like it was more an open-ended question
111
that meta threw back at me like hey what should we do in this specific edge case
112
or what should we do in this scenario
113
and then i had to type it out you think about it versus like take one of their pre-camp answers
114
so i probably would have one like relied on myself for
115
more product thought process than thinking the llm would work with me
116
and then two like honestly from from a technical perspective like
117
i would have brushed up more on how to actually optimize for token usage latency retrieval like
118
that was all at the time like very foreign to me
119
totally hear you one small question i'm kind of using an assumption here
120
because they just added uh ai assisted coding rounds for software engineers at meta
121
and one of the things they let them do is they let them use the exact tool
122
that they're going to use in the interview in their practice i'm not surprised
123
that they're doing
124
that they seem very ai first i've talked to dozens of
125
fang interviewers over the last couple years meta is just in a different freaking league absolutely
126
so i'm gonna guess
127
that they probably would do well to have the pms use the tool beforehand
128
so even if they're not doing that i bet they're gonna do that
129
because that's how it is with the with the software engineering
130
one that's actually a good point that's one thing i probably would have changed as well
131
because they did send me like a demo of the tool beforehand.
132
I think it was like a quick time demo of somebody using the tool and how it works.
133
And it looked very just like any other blog coding.
134
Yeah, it kind of looked a little bit like V0 that Versal makes.
135
And so I practiced a lot on V0 and maybe a little bit of lovable as well.
136
It seemed like it was gated internally besides the one instance they gave me while I was on the call.
137
Open-ended question that no one has a solution to.
138
I want to hear your opinions.
139
When that thing's loading for five to seven minutes, what are you trying to do to add value oh yeah that's a good question
140
when i was going back uh well while i was sort of loading
141
and generating my prototype i went back to my notes
142
and just like i had a lot of notes and things
143
that i would that i kind of called out of scope
144
and i was thinking of ways to include that because i thought
145
that if i had a better prototype that included maybe some additional features
146
that wouldn't have been mvp
147
but would be built into the prototype it would make me
148
look like a well more well-rounded pm that's trying to shoot for the north star um
149
but maybe iterate towards it and And so I was going back and thinking of additional features that I can add.
150
And so I was thinking of ways to kind of increase my scope while that was spinning.
151
And then he was also following up
152
and asking me like what types of things I would include in the user creation onboarding profile.
153
But those were like honestly more product sense questions versus like AI specific questions.
154
I think all the AI specific questions around latency and compute came after the first generation.
155
It was like, okay, if somebody's joining via Facebook or connecting via Instagram and we don't have much data on them, let's say it's like, for lack of a better term,
156
a burner account, how would we even prompt them to fill in some of the required fields
157
that we want versus just sending them directly to the homepage of the app?
158
How do we get more interest or more user input so
159
that we can actually fix the listings algorithm or the listings display to show them like opportunities they would like?
160
And we can't do that when they don't when we don't have the data.
161
So what ways would you incentivize a user to get meta additional data
162
so that we can leverage that to show them a better listing?
163
So cool um another one that i hear about people talking about in
164
while it's loading is like one of the common questions people are asking is like hey
165
if this were actually production app what would you do differently
166
so what do you think of that tip of saying like proactively answering
167
that and saying like just in case they're gonna ask
168
that like hey by the way
169
if i were gonna make this a production level app here's
170
what i would do differently that's i no i didn't i didn't actually consider
171
that i probably should have um at the time i wasn't
172
really i was thinking like we were really doing just a quick
173
and dirty exercise and so i didn't think of really like scalability
174
but in today's day it's obviously like the biggest problem you
175
see like you know amazon fired a bunch of people
176
and then had four step ones or five step ones in prod
177
and it's bigish scalability in general um and
178
so my take on it nowadays is like okay this is
179
very much just a clickable prototype with a lot of functionality
180
that you can see what vision I want
181
and maybe I'll even MCP to Figma to get in my design system
182
and components so that you can see exactly what I'm trying to build towards
183
but you still need to go in
184
and integrate it into the back-end system you need to integrate into the front-end system you need to make sure
185
that the code is actually like functional and it's written in a way
186
that it's not going to create additional tech debt
187
because I think that's a big problem even in my current
188
role today is like the product team will buy code a lot of things
189
and hand it to engineering and engineering will be like, I need two weeks with this because I have to fix a bunch of functions
190
or a bunch of technical things in the code
191
that was written very poorly by AI to just get you this initial prototype.
192
I love those, those two concrete examples of like the real problems people are experiencing on the job
193
and then connecting that to the interview.
194
I think that's a really, really smart tip.
195
I guess my best advice and kind of my reflection on that is like using those tools as much as possible.
196
So, you know, the best things to prompt is a big one.
197
And the second one is also like what data source you're using really goes a long way.
198
Like you can tell it's a data and it'll mock some things up, but it won't really be functional on the prototype unless you have it point somewhere.
199
So what I've started to do in my own time is like, there's like a side furniture project I'm working on.
200
I've had to start pointing to like catalogs in Ikea
201
or sort of crate and barrel or somewhere else to actually pull in real data.
202
Even though it's and it's just from the web, it's at least pulling in like real catalog things that are, that, that exist in those domains.
203
And so that's something that I probably would have done differently.
204
Like I probably would have gone to one of the volunteering boards and pulled in opportunities from there.
205
And I think if I could have somehow like prompt, like even wrote a couple of things about myself before starting it and being like, I want you to use me as sort of the test user, it might've been, it might've flown better.
206
And then, oh, one additional thing that I just, I've totally forgotten.
207
And you kind of asked how I would build that into a mobile space.
208
And if I would add that as part of the current Facebook app or standalone app, I thought that was really interesting.
209
Part of my mind went standalone
210
because it might be easier to implement in a brand new code base and a brand new repo standalone.
211
But at the same time, I think a lot of people already use Instagram, Facebook.
212
There's already a lot of daily active users, monthly active.
213
And so it might make more sense to get a wider top of funnel by using those platforms first.
214
Looking back, I would be more prepared to talk about maybe a mobile standalone product.
215
Even if I did inspect and like show to me and, you know, mobile responsive, it's not the same as a mobile app with the various components and things.
216
And so I thought that was a really interesting question that he threw at me at the end.
217
It's just so cool.
218
You did an amazing job.
Scarica l'app
Tutto ciò che serve per parlare fluentemente
Pronuncia con IAVoto per ogni frase
Pratica IPAPadroneggia ogni suono
VocabolarioCostruisci il tuo lessico
Gioco di vocabolarioImpara giocando
Stesso Canale
C11
Uber Data Engineering Mock Interview - Ride-Sharing Data Warehouse Schema
Interview EnglishUniversity StudentsWorking Adults
B12
What is an API?
University StudentsWorking Adults
C14
System Design Mock Interview: Design Facebook Messenger
Business EnglishUniversity StudentsWorking Adults
C17
How to Answer System Design Interview Questions (Complete Guide)
Business EnglishInterview EnglishUniversity Students
C12
System Design Mock Interview: Design WhatsApp
Business EnglishInterview EnglishUniversity Students
B22
Senior Product Designer Mock Interview: App Critique of Yelp
Business EnglishInterview EnglishUniversity Students
C1Understanding TCP vs UDP
University StudentsWorking Adults
B21
FULL Data Science Mock Interview | SQL, Statistics, Business
Interview EnglishUniversity StudentsWorking Adults
B2Anthropic Culture Fit | Behavioral Interview
Business EnglishInterview EnglishUniversity Students
FOR YOU
Video consigliato
![[2026] TOEFL iBT Speaking Take an Interview | Step-by-Step Strategies #0008](https://img.youtube.com/vi/Bo4ARkCDQLk/mqdefault.jpg)
C12
160 frasi
[2026] TOEFL iBT Speaking Take an Interview | Step-by-Step Strategies #0008
TOEFL PrepBusiness EnglishInterview English
C12
239 frasi
Excellent 2 Years AWS DevOps Engineer Mock Interview with Questions and Answers | Cloud | Docker
Business EnglishInterview EnglishUniversity Students
C1239 frasi
Learn how to SOLVE a data analytics case study problem
Business EnglishInterview EnglishUniversity Students
C14
264 frasi
How Great Software Engineers Are Moving Faster Than Ever
Business EnglishInterview EnglishUniversity Students
C18
324 frasi
Distributed Task Scheduler: System Design Interview (Stripe & Amazon Offers)
Business EnglishInterview EnglishUniversity Students
C12
8 frasi
Google product manager behavioral interview (Top 5 questions)
Interview EnglishUniversity StudentsWorking Adults
C1229 frasi
The Skills You’ll Need to be a Senior Software Developer
Business EnglishUniversity StudentsWorking Adults
C12
95 frasi
API Gateways in System Design Interviews w/ Ex-Meta Staff Engineer
Business EnglishInterview EnglishUniversity Students
C13
715 frasi
Mastering iOS Development: Mock Interview with Ramiro Neto
Business EnglishInterview EnglishUniversity Students
C12
263 frasi
Anthropic's CCA Exam as a Field-Guide for Agentic Engineering — Frank Coyle, UC Berkeley
Business EnglishInterview EnglishUniversity Students
Vocabolario e note di pronuncia per questa lezione
Questo video contiene 218 frasi e 2956 parole da ripetere con lo shadowing. Il parlato dura 13:36. Chi parla va veloce, circa 217 parole al minuto: aspettati suoni legati e ridotti. Il 84% delle parole rientra nelle 3.000 più comuni dell’inglese; conviene studiare le altre prima di iniziare.
Vocaboli chiave di questo video
15 parole del video che vale la pena imparare, con pronuncia e significato:
| Parola | Pronuncia | Significato |
|---|---|---|
| prompt verbo | /pɹɑmpt/ | incitare, guidare |
| prototype sostantivo | /ˈpɹəʊtətaɪp/ | prototipo |
| token sostantivo | /ˈtoʊkən/ | simbolo, segno |
| versus | /ˈvɝsəs/ | contro |
| volunteer sostantivo | /ˌvɑlənˈtɪɹ/ | volontario, volontaria |
| vibe sostantivo | /vaɪb/ | atmosfera |
| differently avverbio | /ˈdɪf.ə.ɹənt.li/ | differentemente, diversamente |
| chart sostantivo | /t͡ʃɑɹt/ | carta, carta nautica |
| clarify verbo | /ˈklæɹ.ɪ.faɪ/ | purificare, chiarificare |
| cloud sostantivo | /ˈklaʊ̯d/ | nuvola, nube |
| efficient aggettivo | /ɪˈfɪʃənt/ | efficiente, performante |
| connect verbo | /kəˈnɛkt/ | connettere, connettersi |
| component sostantivo | /kəmˈpoʊ.nənt/ | componente |
| rely verbo | /ɹɪˈlaɪ/ | fare affidamento, basarsi |
| scope sostantivo | /skəʊp/ | ambito, scopo |
I phrasal verb che sentirai
| Parola | Significato |
|---|---|
| go through verbo | attraversare |
| brush up verbo | rispolverare |
| go back verbo | tornare |
| check out verbo | investigare |
Pronuncia a cui fare attenzione
Chi parla usa 51 contrazioni e forme ridotte, come didn't, don't, I'm. Pronunciale nella forma breve, così come le senti.
- I suoni “th”: algorithm /ˈælɡəɹɪðm̩/, rethink /ɹiːˈθɪŋk/
- I suoni “sh” e “zh”: efficient /ɪˈfɪʃənt/, brush /bɹʌʃ/, assumption /əˈsʌm(p).ʃ(ə)n/, optimization /ˌɑptəmaɪˈzeɪʃən/, consideration /kənˌsɪd.əˈɹeɪ.ʃən/
- Parole lunghe — attenzione all’accento: differently /ˈdɪf.ə.ɹənt.li/, unlimited /ʌnˈlɪm.ɪ.tɪd/, optimization /ˌɑptəmaɪˈzeɪʃən/, ordinary /ˈɔɹdɪnɛɹi/, consideration /kənˌsɪd.əˈɹeɪ.ʃən/
Come esercitarsi con questo video
- Ascolta tutto il video una volta senza parlare e annota le parole che non conosci.
- Inizia a velocità 0,75×, fai shadowing frase per frase e torna alla velocità normale quando diventa facile.
- Registrati e confronta con l’originale, facendo attenzione a parole come prompt, prototype, token.
La grammatica di questo video
Le strutture che chi parla usa di più, con le parole esatte del video:
| Struttura | Nel video |
|---|---|
| Frasi condizionali if + frase, will/would + verbo — una condizione e il suo risultato | if you used it will ask |
| Present perfect have/has + participio passato — un’azione passata che conta ancora adesso | You've done · you've been put · We've built |
| Forma passiva be + participio passato — conta ciò che accade, non chi lo fa | been asked · you've been put · be built |
| Frasi relative who / which + frase — un’informazione in più su una persona o una cosa | norm, which is · resources, which is |
Cos'è la tecnica dello Shadowing?
Shadowing è una tecnica di apprendimento delle lingue supportata da studi scientifici, originariamente sviluppata per la formazione dei traduttori professionisti e resa popolare dal poliglotta Dr. Alexander Arguelles. Il metodo è semplice ma potente: ascolti un audio in inglese di madrelingua e lo ripeti immediatamente ad alta voce — come un'ombra che segue il parlante con un ritardo di solo 1–2 secondi. A differenza dell'ascolto passivo o degli esercizi di grammatica, lo shadowing costringe il tuo cervello e i muscoli della bocca a elaborare e riprodurre simultaneamente i modelli di discorso reale. La ricerca dimostra che migliora significativamente la precisione della pronuncia, l'intonazione, il ritmo, il discorso connesso, la comprensione dell'ascolto e la fluidità del parlato — rendendolo uno dei metodi più efficaci per la preparazione alla prova di speaking dell'IELTS e per la comunicazione reale in inglese.