August 16, 2026 • 11 mins
How much of your week goes into fixing what AI just handed you? Not writing from scratch, just cleaning up, correcting, and nudging a draft that looked fine for about ten seconds before you actually needed it to work.
That endless back and forth has a name. We call it slop fixing, and there is research suggesting it is quietly eating close to a full day of our working week.
In this How I AI episode, Neo and I get into why this happens and the two very different ways of working with AI that decide whether it does. You will hear the difference between gunslinging and architecting, what a proper brief actually sounds like, and how to have a conversation with AI before you ask it to build anything. By the end, you will be getting version one at 90 percent instead of 20.
What you'll learn:
- What actually separates AI slop from work that lands
- The difference between gunslinging and architecting with AI
- What a proper brief for AI actually sounds like
- How to have a real conversation with AI before asking it to build anything
- Why version one can land at 90 percent instead of 20
- Practical AI tools for productivity and focus
- Real-world AI workflows used by high performers
- How to use AI at work without burning out
- Smart shortcuts for managing time and mental load
Connect with Neo Aplin on LinkedIn (https://www.linkedin.com/in/neoaplin/) and via inventium.ai (https://inventium.ai), where he leads Inventium's AI training and upskilling work with organisations and teams.
How I AI with Dr Amantha Imber and Neo Aplin explores how high performers are using AI at work to boost productivity, make better decisions and reduce overwhelm.
Want more practical ways to put AI to work? You can now follow How I AI separately on Spotify or
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
You ask AI to write something, it gives you back
a first draft that looks fine, but the moment you
read it properly, there's just nothing in there. So you
go back and forth with the AI, telling it what's wrong,
and half an hour later you think I should have
just written this myself. There's a research suggesting that we
(00:21):
lose close to one day a week doing exactly that.
Neo and I call this slop fixing, and in this
episode we get into why it happens and the two
ways of working with AI that decide whether it does.
You'll hear the difference between gun slinging and architecting, what
(00:42):
a proper brief actually sounds like, and how to have
a conversation with AI before you ask it to build anything.
By the end of this episode, you'll be getting version
one at ninety percent and not twenty percent. Welcome to
(01:03):
how IAI with me Doctor Amantha Imba and Neo Applin,
head of Inventium AI. Each episode we share one practical
way to use AI better at work and in life.
No fluff, no deech jargon, just things you can use
straight away. I think it's quite well known at this
point that AI has promised a lot of productivity savings,
(01:27):
but it is simply not delivering, and Neo I want
to talk about why and what we can do about it.
So we think about this problem and we've got a
name for it, and it is slop fixing, because if
you are not working well with AI, you are producing
a lot of AI slop. First, let's start by defining
(01:49):
AI slop for those that haven't come across it. What
is AI slop?
Speaker 2 (01:55):
Different definitions for different people, but effectively, it's when you
get something out of AI and it doesn't feel real.
It doesn't feel it doesn't hit the mark, It doesn't
actually drive the conversation forward, the work forward. It's just
a average piece of work which has lots of generalities
in it that people read through and just go, yeah,
it's not quite there.
Speaker 1 (02:16):
It's funny because it kind of looks good on the surface,
but when you get into it, there is absolutely no substance.
And what certainly we observe a lot of people doing
is they will prompt AI poorly. It will produce slop,
and if the person still has their critical thinking brain on,
they will recognize that and then they will just be
(02:38):
going back and forth and back and forth with the AI,
arguing with it, and we call this slop fixing. And
if you don't know how to prompt properly or are
not giving the AI enough context, you can spend hours
and hours of your week slop fixing, Which makes sense
(03:00):
then that we're not seeing these huge productivity gains. So
what's your experience of slop fixing and what you observe
in the many, many workshops you're running an inventive AI.
Speaker 2 (03:12):
There's an argument with AI. People are working with it
and they go, you don't quite get me, and then
they argue with it, like no, no, you've got to do
more of this, You've got to do less of that.
It's almost like you're sculpting the work with AI, and
there's lots of backwards and forwards and a lot of
time to do this. Or people just give up and
they go, eh, it's fifty percent of the way there.
I've got a structure. That's fine. Now you just got
(03:35):
to go and clean that thing up myself. And so
that's slop fixing on my time on my computer. But
I don't know whether people are actually equating the total
amount of time has taken to work with the AI
and then do the cleanup versus just doing it themselves.
Speaker 1 (03:49):
One hundred percent. And what research would suggest, because I've
heard slop fixing referred to as several different terms, is
that we spend nearly one day a week slop fixing.
And so what we want to talk about today is
how can we reduce the time that we are spending
fixing the slop. So let's start with one term. So
(04:10):
we're going to talk about a couple of terms that
we use at Inventium AI, and they are gun slinging
and architecting. So noil can you explain what gun slinging is.
Speaker 2 (04:21):
It's what it's shooting from the hip and then not
quite hitting the bullseye at all times. So a lot
of people when they're working with AI, they'll just go,
I need AI to do some research on this, or
to analyze this thing, you know, or to write this
particular piece or whatever. But they don't give a lot
of background, they don't give a lot of context, they
don't give a lot of goals. There's not a lot
(04:41):
of wist. So you're not spending a lot of time
to work with that brief that you're giving AI to
get it to do the thing, and because you're not
giving it a great brief, just like you would with
any staff member. If you're not giving it a great brief,
they're not going to give you a great piece of
work out. So it's kind of shooting from the hip quick.
You can kind of quick shot from the hip with
your prompt and and then of course you don't hit
the bulls eye many times, and then you get a
(05:03):
fair bit of AI slot that you then got to
work and refine with. So that's gunslinging.
Speaker 1 (05:08):
Okay, So we give a solution and we call it architecting.
So this is a really different way to work with
AI compared to what we observe a lot of people doing.
So can you explain what is architecting.
Speaker 2 (05:23):
I'll give you an analogy of gun slinging and architect
just so you can get around you your head. You
wouldn't go to an architect or to someone who's going
to design a house and say build me a house,
because they'll build you a wonderful house and that's great,
but you go, no, no, no, no no. I wanted to
be a three bedroom house, not a four bedroom house.
I needed two car garage and I want to this,
(05:44):
and I want a larger kitchen, and I want a
Buttler's page. You haven't told the architect what you want
for your house, so why are you getting upset with
this house that you've now magically got. So gunslingers are
more like, build me a house, and then it's oh,
this is a crap house. Let's work on it. Ah,
you've got to do more with it. Whereas with architecture,
you'll go to an architect and you'll say, Okay, i've
got a family, and there's a family of four, and
(06:05):
I've got a dog, so we need a big enough yard.
And my husband he loves tinkering in the garage, so
we need to have a little workhopping area in the garage.
And so you're having a discussion about what you need,
and so you're more likely to get that first house
design really well and the other really good. The other
thing is when you're working with an architect, you'll ask questions.
You'll have a discussion going on, and you say, hey,
(06:27):
would it be better for me to do this? Or
is a light shining from the wrong direction for me
to have this window in this area, and things like that.
So it's it's a discussion with your architect as opposed
to just saying build me in house and then getting
grumpy that you've got a very generic, very bad house.
Speaker 1 (06:43):
So can you perhaps talk me through an example of
using architecting, Like, what's the brief? How are you having
that discussion with the AI? What does that look like?
Speaker 2 (06:56):
All right, let's look at a scenario something like I
don't know, I'm doing an onboarding plan for a new
staff member. Why not? Okay, if you just said, write
me an onboarding plan for a new what would you
like for a staff member? Like a marketing lead? Great,
a marketing lead, yep, okay, So give you a non
(07:17):
boarding plan for a marketing lead. You'll get a pretty
average one. But the first thing is is this a
digital marketing lead or are they dealing with posters and
those kind of marketing things. It's a really different marketing
role if you're doing digital versus non digital. You might
have one workforce one need for your business, which might be, Hey,
(07:39):
what we're trying to do is grow into international areas
and so therefore that's a very different kind of marketing
thing than grassroots marketing. So what you probably do is say,
look what I need is an onboarding to help me
get this marketing lead up to speed. Here's the things
that they're going to do. Here's what I'm trying to
achieve with this. So you're setting the goal, you're having
a conversation with AI about what you really want out
(08:01):
of this, and then what you might do is say, hey,
now this is kind of my goals, what I'm kind
of going for. What's best practice on this, what's the
best way to achieve this? So they are more innovative
ways that I could potentially get them across all their
needs and also to engage with the team and things
like that. And then AI might give you some suggestions.
You might agree with some, you might disagree with others,
(08:21):
but you then got a discussion going on, plus the
ability to be able to change things that it might
suggest that otherwise may not fit for you. So he
I might say, oh, you can do this, this and this,
and the second of those. You might say that doesn't
apply for us. We don't need that because we're doing
international digital marketing. And so you've suggested something that's got
like with posters and bricks and mortar, that's not us.
(08:43):
So you've got an ability here to refine your plan
with AI so it knows your goals what you're trying
to achieve, and then align what you want. And then
when you're happy that you've got this architect this blueprint
that you want if you like, then you'll get it
to build whatever you wanted to build.
Speaker 1 (09:00):
And I think that's the key difference. Like when I've
seen you do architecting, and I know when I do
it myself, it's kind of the opposite to what I
see a lot of people doing, where they'll just go,
here's here's the stuff you need to know. AI, Now
produce the output. And then once you've got the output,
that's where you spend a lot of time arguing and
(09:21):
where a lot of the slot fixing comes in. But
you're saying, have a really long conversation or at least
a moderate length conversation with the AI before you even say, hey,
produce this output. Am I phrasing that correctly?
Speaker 2 (09:37):
Yeah, It's not necessarily about the length. It's more about
the effort bias. So with Gunsley, you've got very amount
of a small amount of effort at the front, but
then you've got a lot of effort down the track
because it's then fixing, and often when you fix one things,
it breaks to something else and then it becomes quite
problematic and then you go, ah, bugg, I'll just go
(09:57):
and do it myself. Whereas with AR it's just a
little bit more time upfront making sure that it knows
what you're really trying to achieve. That goal is really important,
and then what matters to you whether it's like best practice,
or he's engaging with my team, or here's my different
client needs those kind of things, and then you're working
with that way. Then when you are sure that it
knows what you want, then go go and produce version
(10:20):
one of this thing. Version one is more likely to
nail what you need to be specific for what you need,
not there's kind of one on one general kind of stuff,
and it's going to be closer to rather than fifty
percent of the way there, it's probably going to be
eighty or ninety percent of the way there. So let's
clean up at the other end. So it's more about
spending a little bit more time upfront and then you've
got a much better result at the end.
Speaker 1 (10:41):
Okay, are there any other final tips on how we
can become better architects with AI?
Speaker 2 (10:48):
One of the ways to approach this is to work
with AI like you would a consultant, a staff member,
those kind of things. So we wouldn't just go, hey,
you do this. Instead, what you'd say is, here's what
I'm trying to check. Here's some background. What do you
think are there any better ways that you think that
we might be able to do this? Oh yeah, but
that doesn't apply for us. But that does. It's a
(11:09):
really good idea. Let's include some of that. And then
it's almost like you're planning things out together, you and
AI as a collaborative team here, and then when you're
comfortable that you know what it knows, that it knows
what you want, then you go, let's produce this thing together.
Speaker 1 (11:27):
Love that. Thank you for giving us at a solution
to all the slot fixing that is consuming so many
people's days. How AI was hosted by me Amantha Imber
and Neo Applan. A big thank you to Martin Imba
who does our sound editing, and Jim Rubio for production support,
and thank you to John Kilby who composed the theme music.
You ask AI to write something, it gives you back
a first draft that looks fine, but the moment you
read it properly, there's just nothing in there. So you
go back and forth with the AI, telling it what's wrong,
and half an hour later you think I should have
just written this myself. There's a research suggesting that we
(00:21):
lose close to one day a week doing exactly that.
Neo and I call this slop fixing, and in this
episode we get into why it happens and the two
ways of working with AI that decide whether it does.
You'll hear the difference between gun slinging and architecting, what
(00:42):
a proper brief actually sounds like, and how to have
a conversation with AI before you ask it to build anything.
By the end of this episode, you'll be getting version
one at ninety percent and not twenty percent. Welcome to
(01:03):
how IAI with me Doctor Amantha Imba and Neo Applin,
head of Inventium AI. Each episode we share one practical
way to use AI better at work and in life.
No fluff, no deech jargon, just things you can use
straight away. I think it's quite well known at this
point that AI has promised a lot of productivity savings,
(01:27):
but it is simply not delivering, and Neo I want
to talk about why and what we can do about it.
So we think about this problem and we've got a
name for it, and it is slop fixing, because if
you are not working well with AI, you are producing
a lot of AI slop. First, let's start by defining
(01:49):
AI slop for those that haven't come across it. What
is AI slop?
Speaker 2 (01:55):
Different definitions for different people, but effectively, it's when you
get something out of AI and it doesn't feel real.
It doesn't feel it doesn't hit the mark, It doesn't
actually drive the conversation forward, the work forward. It's just
a average piece of work which has lots of generalities
in it that people read through and just go, yeah,
it's not quite there.
Speaker 1 (02:16):
It's funny because it kind of looks good on the surface,
but when you get into it, there is absolutely no substance.
And what certainly we observe a lot of people doing
is they will prompt AI poorly. It will produce slop,
and if the person still has their critical thinking brain on,
they will recognize that and then they will just be
(02:38):
going back and forth and back and forth with the AI,
arguing with it, and we call this slop fixing. And
if you don't know how to prompt properly or are
not giving the AI enough context, you can spend hours
and hours of your week slop fixing, Which makes sense
(03:00):
then that we're not seeing these huge productivity gains. So
what's your experience of slop fixing and what you observe
in the many, many workshops you're running an inventive AI.
Speaker 2 (03:12):
There's an argument with AI. People are working with it
and they go, you don't quite get me, and then
they argue with it, like no, no, you've got to do
more of this, You've got to do less of that.
It's almost like you're sculpting the work with AI, and
there's lots of backwards and forwards and a lot of
time to do this. Or people just give up and
they go, eh, it's fifty percent of the way there.
I've got a structure. That's fine. Now you just got
(03:35):
to go and clean that thing up myself. And so
that's slop fixing on my time on my computer. But
I don't know whether people are actually equating the total
amount of time has taken to work with the AI
and then do the cleanup versus just doing it themselves.
Speaker 1 (03:49):
One hundred percent. And what research would suggest, because I've
heard slop fixing referred to as several different terms, is
that we spend nearly one day a week slop fixing.
And so what we want to talk about today is
how can we reduce the time that we are spending
fixing the slop. So let's start with one term. So
(04:10):
we're going to talk about a couple of terms that
we use at Inventium AI, and they are gun slinging
and architecting. So noil can you explain what gun slinging is.
Speaker 2 (04:21):
It's what it's shooting from the hip and then not
quite hitting the bullseye at all times. So a lot
of people when they're working with AI, they'll just go,
I need AI to do some research on this, or
to analyze this thing, you know, or to write this
particular piece or whatever. But they don't give a lot
of background, they don't give a lot of context, they
don't give a lot of goals. There's not a lot
(04:41):
of wist. So you're not spending a lot of time
to work with that brief that you're giving AI to
get it to do the thing, and because you're not
giving it a great brief, just like you would with
any staff member. If you're not giving it a great brief,
they're not going to give you a great piece of
work out. So it's kind of shooting from the hip quick.
You can kind of quick shot from the hip with
your prompt and and then of course you don't hit
the bulls eye many times, and then you get a
(05:03):
fair bit of AI slot that you then got to
work and refine with. So that's gunslinging.
Speaker 1 (05:08):
Okay, So we give a solution and we call it architecting.
So this is a really different way to work with
AI compared to what we observe a lot of people doing.
So can you explain what is architecting.
Speaker 2 (05:23):
I'll give you an analogy of gun slinging and architect
just so you can get around you your head. You
wouldn't go to an architect or to someone who's going
to design a house and say build me a house,
because they'll build you a wonderful house and that's great,
but you go, no, no, no, no no. I wanted to
be a three bedroom house, not a four bedroom house.
I needed two car garage and I want to this,
(05:44):
and I want a larger kitchen, and I want a
Buttler's page. You haven't told the architect what you want
for your house, so why are you getting upset with
this house that you've now magically got. So gunslingers are
more like, build me a house, and then it's oh,
this is a crap house. Let's work on it. Ah,
you've got to do more with it. Whereas with architecture,
you'll go to an architect and you'll say, Okay, i've
got a family, and there's a family of four, and
(06:05):
I've got a dog, so we need a big enough yard.
And my husband he loves tinkering in the garage, so
we need to have a little workhopping area in the garage.
And so you're having a discussion about what you need,
and so you're more likely to get that first house
design really well and the other really good. The other
thing is when you're working with an architect, you'll ask questions.
You'll have a discussion going on, and you say, hey,
(06:27):
would it be better for me to do this? Or
is a light shining from the wrong direction for me
to have this window in this area, and things like that.
So it's it's a discussion with your architect as opposed
to just saying build me in house and then getting
grumpy that you've got a very generic, very bad house.
Speaker 1 (06:43):
So can you perhaps talk me through an example of
using architecting, Like, what's the brief? How are you having
that discussion with the AI? What does that look like?
Speaker 2 (06:56):
All right, let's look at a scenario something like I
don't know, I'm doing an onboarding plan for a new
staff member. Why not? Okay, if you just said, write
me an onboarding plan for a new what would you
like for a staff member? Like a marketing lead? Great,
a marketing lead, yep, okay, So give you a non
(07:17):
boarding plan for a marketing lead. You'll get a pretty
average one. But the first thing is is this a
digital marketing lead or are they dealing with posters and
those kind of marketing things. It's a really different marketing
role if you're doing digital versus non digital. You might
have one workforce one need for your business, which might be, Hey,
(07:39):
what we're trying to do is grow into international areas
and so therefore that's a very different kind of marketing
thing than grassroots marketing. So what you probably do is say,
look what I need is an onboarding to help me
get this marketing lead up to speed. Here's the things
that they're going to do. Here's what I'm trying to
achieve with this. So you're setting the goal, you're having
a conversation with AI about what you really want out
(08:01):
of this, and then what you might do is say, hey,
now this is kind of my goals, what I'm kind
of going for. What's best practice on this, what's the
best way to achieve this? So they are more innovative
ways that I could potentially get them across all their
needs and also to engage with the team and things
like that. And then AI might give you some suggestions.
You might agree with some, you might disagree with others,
(08:21):
but you then got a discussion going on, plus the
ability to be able to change things that it might
suggest that otherwise may not fit for you. So he
I might say, oh, you can do this, this and this,
and the second of those. You might say that doesn't
apply for us. We don't need that because we're doing
international digital marketing. And so you've suggested something that's got
like with posters and bricks and mortar, that's not us.
(08:43):
So you've got an ability here to refine your plan
with AI so it knows your goals what you're trying
to achieve, and then align what you want. And then
when you're happy that you've got this architect this blueprint
that you want if you like, then you'll get it
to build whatever you wanted to build.
Speaker 1 (09:00):
And I think that's the key difference. Like when I've
seen you do architecting, and I know when I do
it myself, it's kind of the opposite to what I
see a lot of people doing, where they'll just go,
here's here's the stuff you need to know. AI, Now
produce the output. And then once you've got the output,
that's where you spend a lot of time arguing and
(09:21):
where a lot of the slot fixing comes in. But
you're saying, have a really long conversation or at least
a moderate length conversation with the AI before you even say, hey,
produce this output. Am I phrasing that correctly?
Speaker 2 (09:37):
Yeah, It's not necessarily about the length. It's more about
the effort bias. So with Gunsley, you've got very amount
of a small amount of effort at the front, but
then you've got a lot of effort down the track
because it's then fixing, and often when you fix one things,
it breaks to something else and then it becomes quite
problematic and then you go, ah, bugg, I'll just go
(09:57):
and do it myself. Whereas with AR it's just a
little bit more time upfront making sure that it knows
what you're really trying to achieve. That goal is really important,
and then what matters to you whether it's like best practice,
or he's engaging with my team, or here's my different
client needs those kind of things, and then you're working
with that way. Then when you are sure that it
knows what you want, then go go and produce version
(10:20):
one of this thing. Version one is more likely to
nail what you need to be specific for what you need,
not there's kind of one on one general kind of stuff,
and it's going to be closer to rather than fifty
percent of the way there, it's probably going to be
eighty or ninety percent of the way there. So let's
clean up at the other end. So it's more about
spending a little bit more time upfront and then you've
got a much better result at the end.
Speaker 1 (10:41):
Okay, are there any other final tips on how we
can become better architects with AI?
Speaker 2 (10:48):
One of the ways to approach this is to work
with AI like you would a consultant, a staff member,
those kind of things. So we wouldn't just go, hey,
you do this. Instead, what you'd say is, here's what
I'm trying to check. Here's some background. What do you
think are there any better ways that you think that
we might be able to do this? Oh yeah, but
that doesn't apply for us. But that does. It's a
(11:09):
really good idea. Let's include some of that. And then
it's almost like you're planning things out together, you and
AI as a collaborative team here, and then when you're
comfortable that you know what it knows, that it knows
what you want, then you go, let's produce this thing together.
Speaker 1 (11:27):
Love that. Thank you for giving us at a solution
to all the slot fixing that is consuming so many
people's days. How AI was hosted by me Amantha Imber
and Neo Applan. A big thank you to Martin Imba
who does our sound editing, and Jim Rubio for production support,
and thank you to John Kilby who composed the theme music.
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