AI Interview Guide for Candidates and Recruiters
Key Points
- Both employers and job seekers are increasingly relying on AI in hiring, but most are using it poorly, leading to sub‑optimal outcomes.
- A large majority of companies (≈83%) and candidates (≈65%) admit to AI‑based screening and applications, often masking the true extent of its use.
- AI‑driven interview platforms frequently create a frustrating candidate experience, with interviewers talking over applicants and generating confusing, poorly recorded interactions.
- Effective hiring should focus on extracting genuine human signals from the “AI noise,” requiring specific strategies for both interviewers and interviewees.
- Candidates should prioritize affordable, purpose‑built tools that help structure their thinking (e.g., free Google interview warm‑up) rather than expensive, generic AI services, remembering that the right tool alone won’t secure a job.
Sections
- Human‑Centric AI Hiring Guide - The speaker outlines a dual‑sided interview framework that helps both candidates and recruiters navigate AI‑augmented hiring while preserving authentic human interaction.
- Crafting a Proof‑of‑Work Portfolio - Guidance on assembling a proof‑of‑work packet that showcases your decision‑making, trade‑offs, and authentic thinking to differentiate you in tech interviews across all roles.
- Transparent AI Use in Hiring - The speaker urges candidates to openly disclose their AI tools and decision‑making using the STAR‑C framework, and advises hiring managers to reward such transparency rather than penalize it.
- Assessing AI Fluency in Hiring - The speaker outlines a hiring framework that uses deliberately messy exercises to evaluate candidates’ AI literacy and integration abilities, emphasizing tool selection, hallucination verification, workflow design, error handling, and systematic collaboration.
- Assessing AI Integration Skills - The speaker explains how to interview candidates by probing AI resistance, testing integration fluency, and spotting red and green flags based on their grasp of AI limitations and problem‑solving ability.
- Using AI Transparently in Hiring - The speaker urges both hiring managers and candidates to openly discuss their AI strategies and master effective prompting, so AI can responsibly enhance résumé assessment and interview preparation.
Full Transcript
# AI Interview Guide for Candidates and Recruiters **Source:** [https://www.youtube.com/watch?v=qVufzX_8bqE](https://www.youtube.com/watch?v=qVufzX_8bqE) **Duration:** 00:18:12 ## Summary - Both employers and job seekers are increasingly relying on AI in hiring, but most are using it poorly, leading to sub‑optimal outcomes. - A large majority of companies (≈83%) and candidates (≈65%) admit to AI‑based screening and applications, often masking the true extent of its use. - AI‑driven interview platforms frequently create a frustrating candidate experience, with interviewers talking over applicants and generating confusing, poorly recorded interactions. - Effective hiring should focus on extracting genuine human signals from the “AI noise,” requiring specific strategies for both interviewers and interviewees. - Candidates should prioritize affordable, purpose‑built tools that help structure their thinking (e.g., free Google interview warm‑up) rather than expensive, generic AI services, remembering that the right tool alone won’t secure a job. ## Sections - [00:00:00](https://www.youtube.com/watch?v=qVufzX_8bqE&t=0s) **Human‑Centric AI Hiring Guide** - The speaker outlines a dual‑sided interview framework that helps both candidates and recruiters navigate AI‑augmented hiring while preserving authentic human interaction. - [00:03:58](https://www.youtube.com/watch?v=qVufzX_8bqE&t=238s) **Crafting a Proof‑of‑Work Portfolio** - Guidance on assembling a proof‑of‑work packet that showcases your decision‑making, trade‑offs, and authentic thinking to differentiate you in tech interviews across all roles. - [00:07:08](https://www.youtube.com/watch?v=qVufzX_8bqE&t=428s) **Transparent AI Use in Hiring** - The speaker urges candidates to openly disclose their AI tools and decision‑making using the STAR‑C framework, and advises hiring managers to reward such transparency rather than penalize it. - [00:10:13](https://www.youtube.com/watch?v=qVufzX_8bqE&t=613s) **Assessing AI Fluency in Hiring** - The speaker outlines a hiring framework that uses deliberately messy exercises to evaluate candidates’ AI literacy and integration abilities, emphasizing tool selection, hallucination verification, workflow design, error handling, and systematic collaboration. - [00:13:20](https://www.youtube.com/watch?v=qVufzX_8bqE&t=800s) **Assessing AI Integration Skills** - The speaker explains how to interview candidates by probing AI resistance, testing integration fluency, and spotting red and green flags based on their grasp of AI limitations and problem‑solving ability. - [00:16:40](https://www.youtube.com/watch?v=qVufzX_8bqE&t=1000s) **Using AI Transparently in Hiring** - The speaker urges both hiring managers and candidates to openly discuss their AI strategies and master effective prompting, so AI can responsibly enhance résumé assessment and interview preparation. ## Full Transcript
If you are hiring or if you are
interviewing, this is your interview
guide. I'm making it for both because
both sides are responsible for using AI
better. And I want to talk about it
because everybody's using AI and most of
us are using it badly. That includes
hiring folks and candidates. 83% of
companies admit to screening with AI. I
bet the others do anyway. 65% of
candidates admit to applying with AI. I
bet the others do anyway. Everyone
sounds the same. Let's say you get past
the application process. Now you have
candidates using tools to interview. And
you know what? Interviewers catch them.
And candidates have a terrible
experience because they're not even
talking to humans anymore. I know a
senior engineer who has over a decade of
experience who recently got rejected
because the AI interviewer talking to
him talked over him wouldn't let him
finish his sentence asked him confusing
questions and it's not even clear it
recorded it correctly and this is
passing for efficiency. I'm seeing case
studies here where companies are
celebrating the efficiencies they get
with AI hiring when people are all over
Reddit and all over X talking about how
terrible the experience is for
candidates. This is not how you get your
next champion if you are hiring. It
doesn't work that way. We need an
interview process that prioritizes human
signal amidst the AI noise. And I want
to give you specific strategies both if
you're an applicant, which we'll do
first, and also if you are hiring, which
we'll do second. And I want to go into
both because I think both sides have a
responsibility to get better here. So
number one, if you're an applicant,
these are my top tips for how you
interview better. Number one, fix your
tool strategy. There are a lot of very
expensive tools out there. Final Round
AI runs over a hundred bucks a month. I
think it's 148 or something.
ridiculously expensive, but they're
preying on the fact that you need a job.
You don't have to use the most expensive
tool. In fact, there are reports from
the employer side of detecting final
round AI interview responses because
they sound so generic. Whereas,
candidates are saying that a much
cheaper alternative like Bay YZ AI is
working better because the answers are
fast and feel fluent and natural. The
point is not to pick a cheating AI that
helps you cheat undetectably. The point
is to find something that you can
partner with that helps you to structure
your thinking. I actually think the most
useful tool may be free. Google
interview warm-up lets you practice and
get better with AI answers. It helps you
understand what you did right and what
you did wrong. It helps you to go back
and forth and spar in a way that's low
stakes. You don't have to pay a lot to
get help. Before we go further, I want
to underline something like three or
four times. The right tool will not get
you the job. And the right tool will not
get you the career. And the tool people
are selling you lies if they say so.
That is not what gets you a sustainable
career. Figuring out how to showcase who
you are, your passion, your genuine
skills, your insights, that's what helps
you win. And AI is only there to help
you do that well. And the prompts that
I'm writing for candidates in this piece
are prompts that I am designing so that
you can prepare better than anyone else
prior to the interview with the help of
AI. Let's get into your artifact
strategy next. Almost no one has an
artifact strategy. So tip number one,
get an artifact strategy. What's an
artifact? An artifact is a proof of
work. It's it's a packet. It helps you
to show your thinking around real
problems. And by the way, that is going
to help you prepare for interviews. As
an example, you would want to look at a
project you've done and not just do what
so many people do, which is throw up a
nice little website, put up a bar chart,
say you made it go up and to the right.
Instead, you want to build a proofof
work packet that shows how you actually
think, the constraints that you faced,
the decisions you made, the trade-offs
that you considered. Traditionally in
product management, we've been doing
this for a while because we were always
told you have to show your thinking as a
PM. I would now say looking at how
people are actually interviewing, that
is more and more the case for every role
in tech. If you're in design, you're
going to need to do this. If you're in
engineering, you'll need to do this in
your own way on the technical side. Even
in roles like customer service and
sales, you are increasingly going to be
asked to show solid evidence of human
judgment. Especially as you get into
more senior roles, you want to be in a
place where you can show that thinking
clearly. And it doesn't necessarily mean
that you just email this packet off and
hope that that works well. I'm not
saying that. It's in the interview. You
have the option to pull it up if it's
interesting and moves the conversation
forward. It acts as an after interview
additional packet of information if the
interviewer is interested. And it helps
you most of all to get prepared without
sounding like a parrot. And so many of
the issues with these AIs that assist
you in interviews is that they make you
sound like a parrot and you are so
desperate to answer the question right,
you don't realize you sound like
everybody else. You should also include
ugly artifacts, not just the pretty
ones. I actually look when I get
resumes, when I look at the websites
people send me, I want to see is
everything super polished or are you
courageous enough to show things you've
worked on, scratch notes, iteration
history, failed experiments. The most
compelling story I have ever seen on a
personal website for a job was this
lengthy single page post. And it showed
a 5-year history in a role. And it went
through meticulously what the person had
done to add value at each stage in that
role. And it had pictures and visuals
and designed elements. And it read
really fluently. And you could see how
the person had negotiated setbacks and
obstacles along the way to get the
company to where it was. It was
incredibly compelling. It showed
iteration. It unquestionably proved
authenticity. The last thing I want to
call out is that the artifact strategy
extends into how you interview. I I call
it the star C method. If you've ever
done STAR, you know it's situation task
and then you go from there into the
assignment and your response. And I'm
adding constraints. And so star C is all
about showing that you can work within
constraints because AI answers
classically are not very
constraintheavy. And so what I recommend
that you do is that you take your star
situation and you want to make sure that
you layer in the constraints that
enabled you to make hard tradeoffs along
the way because good constraints, if
properly told in the STAR format so
people can follow along, help you show
good judgment. Good constraints help you
show good judgment. And I think that
that's increasingly important because if
you're just giving a standard response
and the interviewer has heard star
before and all the AIs have heard star
and you tell star, it feels very stale.
You need something that helps you to add
that human element. And if you remember
star C, it can help. So situation, task,
action, results, and make sure you layer
in those constraints. That's the C. I
want to go beyond just the toolkit and
the artifacts and interview strategy for
a minute with candidates. If you are
using AI,
please be transparent in 2025. It
actually increases your authenticity.
Let me give you an example of some talk
tracks that would impress me. I use
Claude for research. I went back to
primary sources. I looked through what
actually worked and what didn't work.
The analysis that I'm putting in front
of you is mine and I made sure that I
can own it and stand behind it.
Fantastic. Show the AI stack that you're
using in the verification process you
use. This is going to be true in
technical roles and non-technical roles,
too. Make sure you mention places where
you disagreed with AI. May make sure you
mention where you caught AI in
hallucination. Make sure you mentioned
what tools you wanted AI to use versus
not. That conversation is important. And
that actually is a nice segue brings me
to the second part of this video where
we're going to talk to hiring managers.
Hiring managers, you need to evaluate
better. And it starts with not
penalizing people for exactly what I
described. If your candidate talks about
using AI, don't you dare penalize them.
Especially if they're being transparent.
That is the kind of culture you want to
have in your company. You want AI
champions who can talk about their
successes with AI and also their
failures with AI. Make sure that you
don't penalize candidates who are
showing that behavior. And so this
brings me to the next piece. If you
actually want candidates who work with
AI, you need to stop running interview
processes that are designed to have zero
AI. So, I'm suggesting that you stop
with your AI detection practices and
start with AI assessment practices. Give
candidates AI tools during interviews.
Meta actually does with this with their
engineers. They give them a llama
install and they tell them to work with
AI and assess their ability to do so.
Evaluate how the candidate actually
collaborates with AI. evaluate not just
if they use it, but how they use it to
add value, whether they just do what AI
says or whether they're actually able to
exercise some agency over the AI and
direct it in ways that are useful to get
the overall job done. Make sure that you
also test how they handle really messy
problems that require conflicting
requirements, high thinking quality, and
the ability to negotiate multiple
constraints. Those are classical areas
where AI breaks down. I just advised
candidates who are interviewing to call
out constraints with the star C method.
I am suggesting to hiring managers that
you fish for those constraints. Look for
messy problems because the candidates
will have to show they are good at what
they're doing with their human brains to
answer messier data problems. Don't just
give a candidate a really clean data
problem as a take-home exercise and
expect to get useful value. In fact,
take-home exercises are on the decline
precisely because AI can get them done.
What I'm advocating is that you give
them exercises that are kind of a mess
because you're testing their ability to
use human judgment. And candidates, if
you're still listening, I'm sorry,
you're going to get some exercises that
are a bit of a mess. But on the plus
side, it gives you the chance to show
your human skill sets. And that's what
we're here to demonstrate. I want to
give you also a framework as a hiring
manager to assess candidates for AI
fluency. It's one of the hottest topics
in 2025. I'll probably do more on it
soon, but as a quick rule of thumb, you
want to be checking for three levels.
One is AI literacy. I guess zero is no
AI, but one is AI literacy where you are
able to see that the candidate can
choose between different tools
intelligently. The candidate can verify
outputs for hallucinations. The
candidate has awareness of AI
limitations. The candidate can tell you
the difference between claude and chat
GPT and why. Number two is AI
integration, which can be technical or
non-technical depending on the role. But
you're looking for the candidate who can
talk about their workflow design or how
they would design workflows in your role
with AI at the heart of those workflows,
what tools they would select, why, how
they would handle data. You want to
check for error handling and have the
candidate bring that up proactively.
Talk about their evaluation and metrics
philosophy. Talk about systematic
collaboration. If you want someone who
can actually help you be the 5% in the
MIT study, that's someone who can help
you with workflows. That infamous study
with execs that said only 5% of projects
deliver ROI. The key was good
integration. Level two candidates on AI
are going to be able to talk integration
fluently. And yes, you want to be asking
interview questions that test for that.
You don't want to just ask your
traditional role interview questions.
Level three AI leadership. This is going
to be for senior roles. You need someone
who can do one and two. So they can do
tool selection, output verification with
their eyes closed. They can walk through
workflow design, error handling, but
they can do more. They can talk to you
about strategic adoption. They can talk
to you about AI governance. They can
talk to you about team development with
AI very fluently. They can architect
systems that allow others to design
workflows and ensure and be accountable
for outputs against multiple workflows
that are designed. These are the kinds
of people that you're looking for in
leadership roles where they understand
the domain, but they also have a very
high level understanding of AI that
allows them to truly lead their team.
Because these days, most people hiring
for leadership roles need a leader
coming into the space that doesn't need
their handheld on AI. They need to be a
champion for AI from day one and
potentially be a champion in a room full
of people where some of them are deep
domain experts but may not be deeper on
AI. And so every hire you make as a
hiring manager needs to move the ball
forward on your AI transformation
strategy and that includes senior
leadership roles. Expect your senior
leaders to know how to develop their
teams on AI from day one. Don't tolerate
ramp time. Ask the questions you need to
ask to ensure that they can do so. As an
example of a good question, why don't
you give them the actual stack you have?
Not the ideal stack, the actual stack
that you have. give them an example of
the kinds of resistance you're seeing
across your organization with AI and
then say how would you solve this? How
would you bring your team along? Let's
say we needed the team to get to strong
integration fluency within 2 months.
What would you do? Why? Cuz you're then
testing multiple things, right? You're
testing domain expertise. You're testing
their uh fluency with AI and how they
would handle that. And you're also
assessing leadership and change
management. And you can break down that
answer and see where they stumble, see
where they're weak, see where they're
strong. There's other questions, but you
get the idea. When you are interviewing,
and by the way, if you're still
listening, this is like free intel for
the candidates. There are red flags and
green flags. And we've always had that,
right? The AI red flags look a little
different. And the AI green flags look a
little different. And I want to spend
some time for AI. A red flag looks like
not just generic responses which I
talked about at the top where you're
overrelying on tools and like you're
just reading the response which yes
people can read body language they can
read when you're like sliding your eyes
to the side they can read the weird
pauses people notice. Okay, candidates,
if your candidate can't explain AI
limitations, if your candidate can't go
off script, if your candidate doesn't
have the ability to break down a problem
from a different angle on fairly short
notice, that is a big red flag. It's
also a great tell because AI tends to
take some time to break down problems
from different angles and is actually
even the most cutting edge models are
not super good at that right now. they
tend to get stuck in the middle of a
chat on a certain angle and anchor to
that because of the context window. And
so if you are suspecting that your
candidate is just reading answers, if
you shift the angle of the problem
quickly, their AI may not catch up. It's
a way to push them off script. On the
other hand, in the AI world, we have new
kinds of green flags. If your candidate
volunteers, how they're catching AI
errors, if your candidate volunteers to
talk about how they do a systematic
verification process for work they get
done so that they take ownership and
accountability for it and they're not
just paring what AI says. If your
candidate can quantify AI impact and
talk both at the individual level and
the team level and the organizational
level about what AI can do for the
business, what AI has done in their
role, that's a huge green flag. If your
if your candidate has a philosophy of
the role that says this is how this role
is evolving in the age of AI that they
can explain coherently they can act as a
peer champion for AI for their role.
It's really compelling. So there's lots
of green flags too. It's not just red
flags here for both parties. Right?
We're bringing this to a close here for
candidates for hiring managers. I want
to give you three principles to stick
with that I think will help you. Three
principles to unlock what feels like a
stuck market right now. Number one,
enhancement beats replacement. AI is
there to make human judgment clearer,
not to substitute for it. Candidates,
that means if you're reading answers and
not using your brains, you're losing.
Hiring managers, that means if you're
using AI to evaluate interview
transcripts and you're not actually
thinking about what the candidate is
saying and taking the candidate
seriously as a person, if you're just
using AI to interview, you are also
losing. You're also losing. You're
contributing to the problem. Both sides
win with transparency. That's number
two. Candidates need to show better
judgment when they admit to using AI.
And managers must find better talent
when they have to talk about how they
actually use AI at work. So bring AI to
the table. Don't hide it. Candidates
don't hide it. Hiring managers don't
pretend it's not there. Both sides need
to talk about their AI strategy to
actually move the ball forward. Third,
last but not least, make sure that you
know how to use your prompts well. I've
included a bunch of I think nine s
prompts that like dig deep on interview
prep. But you need to have prompts that
actually help you move the ball forward.
If you're assessing résumés with prompts
as an aid, not as a substitute, you need
to have prompts that actually help you
to do that. If you are preparing as a
candidate, you need to have prompts that
help you to research a JD and go way
beyond the surface level in order to
stand out as a candidate. I they have
written prompts where you can get three
or four pages of really strong interview
prep material out of one job description
because you're telling the AI very
specifically what to look for that helps
you to prepare. It's all about intent.
It's not like my words are not magic.
It's about telling the AI how to
effectively assess
what is in front of it, what is between
the lines, and what you need as an
interview prepper to get ready for a big
conversation. Hiring is broken, kind of
broken. I want it to get better. And I
think the only way it can get better is
if we admit that AI is at the table now.
If we're transparent about it, and if we
use AI to support human judgment rather
than to replace it. Best of luck out
there, and let me know how you're doing.
Cheers.