Goldilocks Prompting: Finding the Sweet Spot
Key Points
- Goldilocks prompting means providing just enough context and guidance for the model to understand the task without overloading it with excessive detail.
- Over‑prompting (too long or overly specific) consumes more tokens, can cause memory issues, and stifles the model’s creativity, while under‑prompting leaves the model to make unfounded assumptions.
- The optimal prompt balances clarity on goals, required tools, and direction without exhaustively listing every minute instruction (e.g., a concise “Make me a good PowerPoint for the board” rather than specifying every slide element).
- In practice, only about 20% of requests need high specificity, whereas roughly 80% benefit from a “Goldilocks” level of detail that is both efficient and allows creative output.
- Crafting a prompt at this optimal altitude is surprisingly challenging and requires careful judgment of how much detail is truly necessary.
Sections
- Finding the Sweet Spot in Prompting - The speaker explains Goldilocks prompting—a balanced approach that provides enough context and guidance without overloading the model, leading to clearer, higher‑quality outputs.
- Good, Bad, Ugly Prompting - The speaker illustrates how prompt detail, context, and token limits differentiate effective (good) prompts from ineffective (bad) and overly complex (ugly) examples for Claude.
- Prompting for Better Newsletter Layouts - The speaker explains how concise, targeted prompts for layout, color, and font guide AI models like Claude or ChatGPT to produce more readable, well‑styled newsletters, using a Thanksgiving example to demonstrate the improvement.
- Pragmatic Architecture and Goldilocks Prompting - The speaker urges developers to avoid defaulting to complex patterns like microservices or repository layers until needed, advocating simple solutions first, and introduces “Goldilocks prompting” — crafting optimally sized prompts that steer LLMs toward the right level of abstraction while remaining reusable across different models.
Full Transcript
# Goldilocks Prompting: Finding the Sweet Spot **Source:** [https://www.youtube.com/watch?v=XfcZujr426o](https://www.youtube.com/watch?v=XfcZujr426o) **Duration:** 00:12:22 ## Summary - Goldilocks prompting means providing just enough context and guidance for the model to understand the task without overloading it with excessive detail. - Over‑prompting (too long or overly specific) consumes more tokens, can cause memory issues, and stifles the model’s creativity, while under‑prompting leaves the model to make unfounded assumptions. - The optimal prompt balances clarity on goals, required tools, and direction without exhaustively listing every minute instruction (e.g., a concise “Make me a good PowerPoint for the board” rather than specifying every slide element). - In practice, only about 20% of requests need high specificity, whereas roughly 80% benefit from a “Goldilocks” level of detail that is both efficient and allows creative output. - Crafting a prompt at this optimal altitude is surprisingly challenging and requires careful judgment of how much detail is truly necessary. ## Sections - [00:00:00](https://www.youtube.com/watch?v=XfcZujr426o&t=0s) **Finding the Sweet Spot in Prompting** - The speaker explains Goldilocks prompting—a balanced approach that provides enough context and guidance without overloading the model, leading to clearer, higher‑quality outputs. - [00:03:25](https://www.youtube.com/watch?v=XfcZujr426o&t=205s) **Good, Bad, Ugly Prompting** - The speaker illustrates how prompt detail, context, and token limits differentiate effective (good) prompts from ineffective (bad) and overly complex (ugly) examples for Claude. - [00:06:39](https://www.youtube.com/watch?v=XfcZujr426o&t=399s) **Prompting for Better Newsletter Layouts** - The speaker explains how concise, targeted prompts for layout, color, and font guide AI models like Claude or ChatGPT to produce more readable, well‑styled newsletters, using a Thanksgiving example to demonstrate the improvement. - [00:09:56](https://www.youtube.com/watch?v=XfcZujr426o&t=596s) **Pragmatic Architecture and Goldilocks Prompting** - The speaker urges developers to avoid defaulting to complex patterns like microservices or repository layers until needed, advocating simple solutions first, and introduces “Goldilocks prompting” — crafting optimally sized prompts that steer LLMs toward the right level of abstraction while remaining reusable across different models. ## Full Transcript
We're gonna talk about Goldilocks
prompting. So, Goldilocks prompting is
the idea that you can prompt too much
and you can prompt too little. I know
that might sound funny to some of you
because I'm the guy who does the prompts
and people think I'm known for these
long prompts. I am here to help you
prompt more effectively. And I want to
remind you that there is an optimal
level of clarity for the goals that you
set out to accomplish with the model.
And you can be over clear, you can be
over long. And so we're going to talk
about Goldilocks prompting and why it
makes such a difference. I'm actually
going to show you an example of the
incredible improvement in model output
quality that you can get when you
actually use Goldilocks prompting. What
is Goldilocks prompting? Very simply, it
is giving the model enough context so it
doesn't assume stuff about you that
isn't true and the problem. and then
giving the model enough guidelines that
it knows the direction to go in. It also
includes giving it like clarity on what
tools it can or should use etc. It is
not exhaustively listing every single
thing you want done. So if you're making
a PowerPoint for example, you could say
I want you to make the font exactly
this. I want you to make every single
slide exactly in this way with this
headline size with this layout with this
particular bullet style with each of
these individual bullets in exactly this
text. Here's the pie chart that goes on
slide seven. You get the idea. Or you
could say, "Make me a PowerPoint. Make
it good. The board's going to be looking
at it." We would probably not do that
one, but we might be tempted to do the
make it specific. And one of the things
that I've been thinking about a lot as
I've wrestled with prompting and context
engineering over the past couple of
months, call it longer than that really.
There is an optimal level of detail and
there's a tradeoff involved. If you want
to give the model as much clarity as I
described where you're describing every
minute detail, the model will go there,
especially the newer ones. It will
increase the token burn so you're more
likely to run into memory issues. it
will reduce the creativity because
you're not engaging the creative
circuits, for lack of a better term, of
your model. So, it's a trade-off. You
have to decide, do you want to be so
specific that the model's going to burn
through a lot of context and follow your
exact design and maybe that's not what
you want. Maybe it is. Or do you want to
back off and give a more general purpose
ask that allows the model to be a little
more creative and might be more token
efficient. In my experience, 20% of the
time you do want that level of
specificity. you you're like, "This is
going to be a lot, but I need it to be
exactly like this. Don't mess it up."
And about 80% of the time, you want to
prompt at the right altitude. You want a
Goldilocks prompt. That is actually
tougher than it looks. It's really,
really tough to prompt at the right
altitude because it's so tempting to
either overescribe or underdescribe. I
want to give you some tools to help with
that. And I think one of the things I
can do to help is just to give you a
visual example of what good looks like
in terms of a prompt. This is directly
from Anthropic. I didn't make this one
up. They put it out in public on their
context engineering blog. I thought it
was really helpful. I'll just pop it up
and then we'll keep going and I'll get
to the demo I created shortly. All
right. Here we are.
This is the system prompt example that
Claude is showing us for good, bad, and
ugly. So good is here, bad is here, and
ugly is here. Basically, if you have the
right level of detail, Claude is going
to understand the role it has. I'll just
zoom this in for you. Claude understands
the role it has. It understands the
tools it can call. It understands how it
can respond, and it understands the
guidelines. And now we're done. This is
all for like Claude's bakery. It's a
nice madeup example. Uh, this is really
bad. So, this is just a very short
prompt and it doesn't give Claude
anything that it can do to actually be
effective in its role. There's no shared
context that Claude can invoke here. And
this I'm going to call this one ugly
because it's so specific. Here's an
exhaustive list of cases. Here's the
user intent. This this prompt is trying
to do everything. This prompt might
actually be six or eight prompts in a
trench coat and like it just keeps
going. It doesn't want to stop. So
that's a visual example of how much of a
difference it makes to have a prompt
that works well. I am finding that one
of the one of the things I can do to
make prompting easier when you're trying
to prompt at that right altitude is I
set myself a token limit. I set myself a
rough number of tokens that I want to
stay under in order to ensure that I
think at the right altitude for the
prompt. Now again, this is for the 80%
where you want to allow some creativity.
There will be those 20% prompts and I,
you know, I've written those that are
super long and like very detailed and we
can go there. They sometimes consume
more model resources. They can be very
precise. They're a tool in the toolbox.
These 80% prompts can be shorter, easier
to understand, easier to iterate on, and
that's why I call them Goldilocks
prompts. They they're good for a lot of
things. They feel the right size for a
lot of things. I tend to keep these
under 500 tokens. And I want to show you
how much of a difference it makes. And
what we're going to do, we're just going
to have fun with this. I'm going to show
you a vanilla prompt where I just say
make it and it's super vague and I don't
give Claude the context. It's just a
basic prompt. and I'm going to say make
a Thanksgiving newsletter, right?
Because it's Thanksgiving. And you're
going to see what it does. And then I'm
going to show you the difference it
makes when I actually add an extra set
of Goldilocks prompts so that Claude
knows what it's doing. Let's dive in.
Okay, here we are. That prompt on the
left there, can you create a family
newsletter? That's all I gave the model.
And this is what Claude comes back with.
It's super basic. You can see it has a
few visual elements here. And it has
these sort of annoying orange
highlights. It has a spot to add family
photos, but of course that's not
clickable or usable. And the copy is
pretty generic. And that's that. It is a
family newsletter I would not want to
send to my family. But what if we choose
to add a more effective prompt? All
right, here we are looking at the exact
same prompt except I've added some
Goldilocks prompting. In fact, this is a
little bit of an advanced technique. I
have stacked up some Goldilocks prompts.
The advantage of having some shorter
ones is that you can be more effective.
So, I have a layout prompt here that
focuses on non-anoying layouts and
specifies those. I have a color prompt
that talks about the kinds of colors
that would convey trust or be modern or
whatever. I also have a font prompt. All
of these
are important for getting this into
better shape. If we move over here, we
see the font has been chosen carefully.
We see the impact of the layout. We see
the colors are chosen much more
carefully and the overall impression is
readable. I'm not going to say this is
the most beautiful family newsletter
you've seen, but I got to be honest with
you, I have seen much worse formatted
family newsletters. And so, this is not
too bad. It even includes a really handy
like sidebar with a quote that's not
horrific and a nice little footer. So,
what's the point of showing newsletters
about Thanksgiving when you're trying to
learn prompting? You want to start to
get a sense of whether these prompts
make a difference. And what I'm trying
to convey to you is that adding these
like slugs, adding these context
snippets can help Claude or Chat GPT
know the difference and actually build a
better newsletter. And yes, I did try
this on chat GPT as well. And Chad GPT
also was able to code up a nicel looking
newsletter. You can decide whether it's
more or less nice. It's more of a font
heavy approach, but at least it followed
the prompt. It followed the Goldilocks
prompt and it's useful. Here it is. So,
this is Chat GPT's effort to respond to
the exact same prompt. You'll notice
less of a visual element, but they
absolutely like you see the investment
in the fonts. You see some really fun
fonts here. You see the use of that
layout piece. You see the ability to
bring pop outs out. It It's not perfect,
right? Some of this layout stuff I don't
think they've done as good a job on, but
I found it very readable. It was easy to
read and easy to understand, and it was
certainly better than just the vanilla
version. You know, sometimes people see
my demos and they're like, "Nate, I
could make a better newsletter. Why are
you trying to show this?" And the answer
is, you probably could make a better
newsletter. The point is to give you
tools to do that effectively. Fantastic.
Go make a better one. My goal here is to
show you that you can take these slugs
of context and actually use them to make
useful work. You can do this same thing
with business writing, with
documentation standards, with
engineering standards. Basically, you
can take anything that you need Claude
or Chat GPT or Gemini to have an opinion
on that is at the right altitude and
apply this set of principles. I will
show an actual prompt here because I
think that that gets at the principles
that I want to All right, here we have
Claude working on a specific skill. And
yes, if you're wondering if these can be
skills, they can be skills. is a bit
difficult to read, but essentially all
you're doing is telling the LLM what
really matters here. System design
should solve real problems, not
patterns. Avoid premature abstraction.
Never use microservices as a default, a
repository pattern before you have
multiple data sources, etc. In other
words, we are taking things that might
be tempting for LLMs to do because they
converge toward commonly seen patterns
on the web and we're saying not on my
watch and we're giving the LLM examples
of pragmatic architectural choices that
could be better like hey maybe it's a
small enough codebase you should use a
monolith maybe you should just build the
straightforward solution and add
patterns only when you feel pain. So, I
think one of the things I want to call
out here is that what I'm showing you, I
deliberately am covering both code and
design. I'm trying to show you the span,
the tool that I'm giving you. And yes,
I'm putting all of these up into uh both
skills and prompts. They're small enough
that you can literally copy and paste
them as prompts into any LLM, into, you
know, Quen, into Grock, into Chad GBT,
into Gemini, etc. But they're super
powerful because they focus you on the
right level of abstraction. They give
you a feel in your fingertips of what is
the right level of abstraction. That's
what I mean by Goldilocks prompting. You
should start to get a feel for what is
the right sized prompt for that 80% of
use cases where you want to allow the
LLM some creativity and judgment to
solve the problem. I think that's
underprompted. I think we often try to
tell people to use their best judgment
for that. I want to give you a sense
that Goldilocks prompting is a learnable
skill. It's also a sharable skill. I'm
going to share a bunch of these prompts
with you so you can start to build them,
modify them. If you don't like my
examples, use different examples. But if
you keep it to relatively the same
length if you use a similar structure,
you're going to end up in a place where
you have guidelines, useful context, a
scaffold if you will, for large language
models to use, but not so much that it
is a brittle prompt that often fails.
And that's what I want you to have. I
want you to have a toolkit that feels
like a well-worn chisel in the woodshed
and a well-worn hammer. Something you
can use every day for a wide variety of
tasks and not get lost on. So there you
go. That is my plea to start thinking in
terms of the altitude you're at.
Thinking in terms of Goldilocks
prompting, asking yourself, am I
prompting at the right level for the
task I'm asking for? Good luck with
Goldilocks prompting.