शैडोइंग अभ्यास: What REALLY Happens in Meta’s AI Product Sense Round - वीडियो के साथ अंग्रेजी बोलना सीखें
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218 वाक्य
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1
You've done interviews like this before.
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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.
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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,
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and then you walk through it, use cases, segmentation solution.
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And then they stop you at the solution
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and tell you to move over to the meta vibe coding board and start sort of prototyping that solution.
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And so I thought that was really unique.
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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.
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But I'd never been asked that in an interview before.
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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
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so i joined the call um i was interviewing with another senior product manager
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that worked on the team um and he said
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that he's going to give me a prompt not really it's
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going to sound like a normal traditional product sense prompt he wants me to go as far as the solution
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and then stop
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and use 30 minutes to flush out sort of my use cases my clarifying questions my user groups
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and my um and which ones i prioritize for solution stop at the 30 minute mark
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and then go over to uh he sent me a link to like a llama choppa chatbot um
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and he wanted me to vibe code whatever solution i had in mind at
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that point so it was like 30 minutes like sort of traditional product sense kind of
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and then 30 minute prototyping what was the actual prompt prompt
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was you're a product manager at meta you've been put in
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charge of a brand new product for volunteering what would you do
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and why so from there i kind of started with clarifying questions
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I asked if there was any time or resource constraints when I think about solutioning.
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He said, no. I said, will it be built as part of one of Meta's ecosystem of products or standalone?
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He said, you decide.
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And then I said, am I both mobile or web?
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And he said, your decision.
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So the only real constraints were voluntary volunteers and unlimited time and resources, which is not really a constraint,
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but kind of an unlimited constraint and everything else you decide.
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And so they decide to have you use their LLAMA tool.
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They bring you into their tool.
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You use that tool.
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What do you remember in your notes from how the interviewer reacted to how you used AI?
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Specifically, were there moments where they like pushed back on your prompts or outputs?
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So I went through goals.
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I went through user groups.
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I went through pain points and then solutions.
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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.
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I would even say the quad code UX is much better.
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But it was very much like ask a bunch of questions.
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I put in my whole prompt notes, like my assumptions, my pain points, and my user group, as well as my solution.
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Once I put that in, it went to work for like, I want to say five to seven minutes.
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And then from there, I started making iterations.
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And he asked me questions around latency.
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He also asked me questions around why I wouldn't leverage image generation.
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And then he also asked me about what did I think about interference, compute, as well as retrieval.
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So that was all those questions sort of came as follow ups after I had that first prototype up.
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And I clicked through it and kind of showed him what I was thinking and what I wanted to change.
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And he was asking me like, oh, is this the most efficient way to prompt it?
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Like, aren't you using more tokens versus prompting it for this?
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Like maybe, maybe like for a good example was I was trying to get to generate a chart of things
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that the users apply to.
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And he was asking me if I should let the LLM determine what chart to use.
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And so I was prompting for that.
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And he's like, oh, don't you think that'll eat more tokens?
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Like maybe there'll be less latency if you use this type of chart.
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And so there was follow up questions like that.
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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,
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or I was like, about to hit send.
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And he was like, oh, let's rethink this.
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And I was like, oh, okay, let's talk about it.
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That's something that we're hearing.
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And even like all across the rounds nowadays, all these Vibe Coding rounds are so new.
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There's no question banks that people can rely on where they can just like, you know, so I totally hear you.
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There's a real thing.
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People are saying that.
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I want to go back to these follow-up questions are super, super cool.
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Did he ask you another question about latency besides the prompting it in a different way to use less tokens?
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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,
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what's a better way you could have maybe built this prototype with the consideration of CompuPower, token optimization, and latency in general.
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What about your friend?
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Did he give you any insights into maybe the question that he got asked?
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Did he get the exact same prompt about volunteering?
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His prompt was about if Meta wanted to create an app, a map app, how would you go about creating it?
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And he also had 30 minutes to kind of do the traditional sense
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and then bring it over to a live coding session
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and he was asked mainly about retrieval like where is he getting all this data from
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because they don't have a map api right now how would
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you get all the various geographic data how would you get
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coordinate how would you maybe built in like directional uh like features within the app within the map
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and so his was a little different
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and honestly it sounded like a lot of his follow-ups were
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specifically on the actual product versus mine being very much about like
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the LLM tooling and the tool calling and token optimization and latency.
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Like, he didn't get any questions about that.
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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.
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And yours asked about retrieval, but maybe only, like, once.
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Yeah, dude.
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Just, like, where would we retrieve all the—first of all, if we're pulling data from—for all these volunteering opportunities,
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what's the best way to optimize the LLM for retrieval specifically on those?
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And then secondly, if the user applies, what's the best way to actually know that we're sending them down the correct path?
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What would you do differently if you were going to prepare for the round again?
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I would probably, one, brush up really well on my technicals when it comes to just vibe coding in general.
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Like optimizing for specific clarifying questions up front.
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Like even the meta bot, like it didn't really like you know how like cursor
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and cloud code
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if you used it will ask you like a like a
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multiple choice question almost like it was more an open-ended question
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that meta threw back at me like hey what should we do in this specific edge case
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or what should we do in this scenario
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and then i had to type it out you think about it versus like take one of their pre-camp answers
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so i probably would have one like relied on myself for
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more product thought process than thinking the llm would work with me
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and then two like honestly from from a technical perspective like
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i would have brushed up more on how to actually optimize for token usage latency retrieval like
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that was all at the time like very foreign to me
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totally hear you one small question i'm kind of using an assumption here
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because they just added uh ai assisted coding rounds for software engineers at meta
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and one of the things they let them do is they let them use the exact tool
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that they're going to use in the interview in their practice i'm not surprised
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that they're doing
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that they seem very ai first i've talked to dozens of
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fang interviewers over the last couple years meta is just in a different freaking league absolutely
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so i'm gonna guess
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that they probably would do well to have the pms use the tool beforehand
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so even if they're not doing that i bet they're gonna do that
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because that's how it is with the with the software engineering
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one that's actually a good point that's one thing i probably would have changed as well
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because they did send me like a demo of the tool beforehand.
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I think it was like a quick time demo of somebody using the tool and how it works.
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And it looked very just like any other blog coding.
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Yeah, it kind of looked a little bit like V0 that Versal makes.
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And so I practiced a lot on V0 and maybe a little bit of lovable as well.
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It seemed like it was gated internally besides the one instance they gave me while I was on the call.
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Open-ended question that no one has a solution to.
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I want to hear your opinions.
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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
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when i was going back uh well while i was sort of loading
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and generating my prototype i went back to my notes
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and just like i had a lot of notes and things
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that i would that i kind of called out of scope
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and i was thinking of ways to include that because i thought
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that if i had a better prototype that included maybe some additional features
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that wouldn't have been mvp
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but would be built into the prototype it would make me
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look like a well more well-rounded pm that's trying to shoot for the north star um
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but maybe iterate towards it and And so I was going back and thinking of additional features that I can add.
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And so I was thinking of ways to kind of increase my scope while that was spinning.
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And then he was also following up
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and asking me like what types of things I would include in the user creation onboarding profile.
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But those were like honestly more product sense questions versus like AI specific questions.
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I think all the AI specific questions around latency and compute came after the first generation.
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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,
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a burner account, how would we even prompt them to fill in some of the required fields
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that we want versus just sending them directly to the homepage of the app?
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How do we get more interest or more user input so
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that we can actually fix the listings algorithm or the listings display to show them like opportunities they would like?
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And we can't do that when they don't when we don't have the data.
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So what ways would you incentivize a user to get meta additional data
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so that we can leverage that to show them a better listing?
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So cool um another one that i hear about people talking about in
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while it's loading is like one of the common questions people are asking is like hey
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if this were actually production app what would you do differently
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so what do you think of that tip of saying like proactively answering
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that and saying like just in case they're gonna ask
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that like hey by the way
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if i were gonna make this a production level app here's
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what i would do differently that's i no i didn't i didn't actually consider
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that i probably should have um at the time i wasn't
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really i was thinking like we were really doing just a quick
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and dirty exercise and so i didn't think of really like scalability
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but in today's day it's obviously like the biggest problem you
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see like you know amazon fired a bunch of people
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and then had four step ones or five step ones in prod
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and it's bigish scalability in general um and
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so my take on it nowadays is like okay this is
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very much just a clickable prototype with a lot of functionality
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that you can see what vision I want
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and maybe I'll even MCP to Figma to get in my design system
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and components so that you can see exactly what I'm trying to build towards
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but you still need to go in
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and integrate it into the back-end system you need to integrate into the front-end system you need to make sure
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that the code is actually like functional and it's written in a way
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that it's not going to create additional tech debt
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because I think that's a big problem even in my current
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role today is like the product team will buy code a lot of things
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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
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or a bunch of technical things in the code
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that was written very poorly by AI to just get you this initial prototype.
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I love those, those two concrete examples of like the real problems people are experiencing on the job
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and then connecting that to the interview.
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I think that's a really, really smart tip.
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I guess my best advice and kind of my reflection on that is like using those tools as much as possible.
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So, you know, the best things to prompt is a big one.
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And the second one is also like what data source you're using really goes a long way.
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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.
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So what I've started to do in my own time is like, there's like a side furniture project I'm working on.
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I've had to start pointing to like catalogs in Ikea
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or sort of crate and barrel or somewhere else to actually pull in real data.
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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.
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And so that's something that I probably would have done differently.
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Like I probably would have gone to one of the volunteering boards and pulled in opportunities from there.
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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.
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And then, oh, one additional thing that I just, I've totally forgotten.
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And you kind of asked how I would build that into a mobile space.
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And if I would add that as part of the current Facebook app or standalone app, I thought that was really interesting.
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Part of my mind went standalone
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because it might be easier to implement in a brand new code base and a brand new repo standalone.
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But at the same time, I think a lot of people already use Instagram, Facebook.
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There's already a lot of daily active users, monthly active.
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And so it might make more sense to get a wider top of funnel by using those platforms first.
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Looking back, I would be more prepared to talk about maybe a mobile standalone product.
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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.
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And so I thought that was a really interesting question that he threw at me at the end.
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It's just so cool.
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You did an amazing job.
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इस पाठ की शब्दावली और बोलने के सुझाव
इस वीडियो में शैडोइंग के लिए 218 वाक्य और 2956 शब्द हैं। बोला गया हिस्सा 13:36 लंबा है। वक्ता तेज़ बोलता है, लगभग 217 शब्द प्रति मिनट, इसलिए जुड़ी हुई और कमज़ोर ध्वनियाँ ज़्यादा मिलेंगी। 84% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं; बाकी शब्द अभ्यास से पहले देख लेना अच्छा रहेगा।
इस वीडियो की मुख्य शब्दावली
वीडियो के 15 सीखने लायक शब्द, उच्चारण और अर्थ के साथ:
| शब्द | उच्चारण | अर्थ |
|---|---|---|
| volunteer संज्ञा | /ˌvɑlənˈtɪɹ/ | स्वयंसेवक, वालंटियर |
| vibe संज्ञा | /vaɪb/ | माहौल |
| chart संज्ञा | /t͡ʃɑɹt/ | लेखाचित्र, चार्ट |
| cloud संज्ञा | /ˈklaʊ̯d/ | मेघ, बादल |
| brush संज्ञा | /bɹʌʃ/ | ब्रश, बुरुश |
| generate क्रिया | /ˈd͡ʒɛn.ə.ɹeɪt/ | उपजाना |
| nowadays क्रिया विशेषण | /ˈnaʊ.ə.deɪz/ | आज कल |
| unlimited विशेषण | /ʌnˈlɪm.ɪ.tɪd/ | अमित, असीम |
| gate संज्ञा | /ˈɡeɪ̯t/ | फाटक, द्वार |
| ordinary विशेषण | /ˈɔɹdɪnɛɹi/ | साधारण, मामूली |
| resource संज्ञा | /ɹɪˈzɔːs/ | संसाधन |
| database संज्ञा | /ˈdeɪtəˌbeɪs/ | डेटाबेस |
| furniture संज्ञा | /ˈfɝ.nɪ.t͡ʃɚ/ | असबाब, फ़र्नीचर |
| loop संज्ञा | /luːp/ | कुंडली |
| consideration संज्ञा | /kənˌsɪd.əˈɹeɪ.ʃən/ | विचारण |
ध्यान देने योग्य उच्चारण
वक्ता 51 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे didn't, don't, I'm। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।
- “th” की ध्वनियाँ: algorithm /ˈælɡəɹɪðm̩/, rethink /ɹiːˈθɪŋk/
- “sh” और “zh” की ध्वनियाँ: efficient /ɪˈfɪʃənt/, brush /bɹʌʃ/, assumption /əˈsʌm(p).ʃ(ə)n/, optimization /ˌɑptəmaɪˈzeɪʃən/, consideration /kənˌsɪd.əˈɹeɪ.ʃən/
- लंबे शब्द — बलाघात सही रखें: differently /ˈdɪf.ə.ɹənt.li/, unlimited /ʌnˈlɪm.ɪ.tɪd/, optimization /ˌɑptəmaɪˈzeɪʃən/, ordinary /ˈɔɹdɪnɛɹi/, consideration /kənˌsɪd.əˈɹeɪ.ʃən/
इस वीडियो से अभ्यास कैसे करें
- पहले बिना बोले पूरा वीडियो एक बार सुनें और जो शब्द नहीं आते उन्हें लिख लें।
- 0.75× गति से शुरू करें, एक-एक वाक्य की शैडोइंग करें, और सहज लगने पर सामान्य गति पर लौटें।
- अपनी आवाज़ रिकॉर्ड करें और मूल से तुलना करें, ख़ासकर volunteer, vibe, chart जैसे शब्दों पर ध्यान दें।
इस वीडियो का व्याकरण
वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:
| संरचना | वीडियो में |
|---|---|
| शर्त वाले वाक्य if + उपवाक्य, will/would + क्रिया — शर्त और उसका नतीजा | if you used it will ask |
| Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है | been asked · you've been put · be built |
| Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है | You've done · you've been put · We've built |
| Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी | norm, which is · resources, which is |
शैडोइंग तकनीक क्या है?
शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।