You've probably used an AI, right? You asked
it for something, maybe a quick summary, some
code, a social media post. Yeah, and you get
something back. It works, technically. But it's
usually just... Yeah. Okay. Average. That average
result. Well, believe it or not, that's often
not the model's fault entirely. It's more about
the architecture. Architecture. How so? We're
still giving it, like, simple instructions when
we should be thinking in terms of recipes, design
patterns. Okay. Welcome to the Deep Dive. This
is for you, the listener, who's ready to go beyond
just basic prompting. You're looking to build
real AI agency. Exactly. We all know the basic
parts, right? The model, that's the brain, the
tools, and those are the hands, and evals, the
quality checks. Crucial stuff. But those are
just the ingredients. Spot on. Today we're digging
into the structure, the recipes. We're exploring
four critical design patterns that take an AI
agent from just following a command to running
a really sophisticated autonomous workflow. So
we're shifting the focus from what AI can do
to how we make sure it does it well, reliably,
every single time. Let's start with recipe one,
reflection. This one feels like maybe the easiest
entry point, something you can use right away,
no fancy tools needed. Yeah, it's definitely
powerful and accessible. The core idea is simple.
Build in self -critique. Don't just take the
AI's first answer. Okay. Instead, you force it
to review its own work. critique it against specific
rules before it even thinks about rewriting.
Right. Because the usual way, the basic prompting,
it's often too vague. We ask it to write something,
then we just say... Is that good? Make it better.
Exactly. And better could mean anything. So the
AI just kind of fiddles with words. It often
misses the real problem. Yeah. So the pro -level
move here, the expert recipe, is using a specific
structured rubric. A rubric. Like in school?
Sort of, yeah. It forces a structured analysis.
Let's say the AI writes a blog post. Instead
of make it better, you give it, say, three things
to grade itself on. Scale of one to five. OK.
Grade the intro for clarity and hook one to five.
Yep. Then maybe grade the examples used. How
relevant are they? One to five. And the call
to action, is it clear? One to five. Exactly.
You're making the agent switch gears. It goes
from just a fast writing mode. To thinking mode.
Analysis mode. Right, structured analysis. And
the trick often is using structured formats,
things like XML tags or maybe JSON. You put the
draft. Inside draft tags? Inside rubric tags?
Why do those tags matter so much? Why not just
bullet points in the prompt? It's about signaling,
really. When the AI sees those specific tags,
draft, rubric, it knows its job isn't creative
writing anymore. Its job is now logical parsing,
following strict rules. It has to look inside
those tags, find the flaws according to the rubric
before it can try again. It literally needs to
figure out why it got a 2 out of 5 on clarity
before rewriting. That makes a lot of sense.
It's like putting up guardrails. Okay, but let's
talk trade -offs. Doesn't this double the time
and the cost? You're basically running it twice.
It absolutely does increase latency and cost.
Yeah, it's a multi -step process. Takes longer,
uses more tokens, no question. Why do it? Because
you're paying for quality assurance. It's a trade
-off, yes, but the improvement you get. Going
from a med draft to a really solid final piece,
that quality jump usually far outweighs the extra
cost or time. It's an investment then. and getting
it right. Precisely. I have to admit, though,
even knowing this, sometimes I still catch myself
typing that lazy, make -it -better prompt. You
know, before I stop, delete it and actually build
the rubric. It's a habit that's kind of hard
to break. That's honest, and I think a lot of
people can relate. It takes discipline. But OK,
if you had to boil down the immediate benefit
of Recipe 1, what's the core shift for the user?
Quality goes up instantly, because the AI has
to judge itself against clear, strict rules.
OK, so reflection helps the AI improve itself
internally. Recipe two, tool use, is about giving
it external powers. Giving it hands, you said
earlier. Connecting it to the outside world.
APIs, search databases. Exactly. And modern models,
they're pretty smart. They often know they might
need a tool. But just relying on that built -in
knowledge, that zero -shot ability, it can get
tripped up by complex questions. Yeah, like if
you ask something with multiple parts. Right.
You ask, is it going to be cold in Hanoi tomorrow?
And based on that, should I pack a jacket and
maybe an umbrella? Okay, that's a few steps.
Yeah, and the AI might just check the temperature
but completely forget the part about the umbrella.
It gets confused. So the pro -level technique
is using guiding examples. Few -shot learning,
right? Exactly. We don't just tell it the tool
exists. We show it how to think through specific
problems using examples. Like teaching it the
decision process. Precisely. If a user asks that
two -part question, maybe... How much does this
cost and is it in stock? The example teaches
the AI an internal thought process. Like what?
Like, OK, wait, I need the product ID before
I can check price or stock. So step one, I must
use the ask you for info action first. Ah, so
the key isn't just using the tool. It's teaching
the AI when to stop and ask for missing info.
Yes, that dramatically cuts down on errors. On
the AI just guessing or making stuff up. So teaching
the context, the steps needed, makes the whole
system way more predictable and less error -prone.
Totally. It reduces errors drastically. Makes
it reliable. Yeah. Whoa. Just imagine scaling
that. A system built with these robust guiding
examples. Handling, say, a billion complex financial
queries a day. Wow. That level of reliable connection
to real -world systems, that unlocks some serious
business value. Truly powerful stuff. Okay, so
tools give the AI hands. This next recipe, planning.
That sounds like giving it foresight. Yeah, now
we're getting into really autonomous territory.
The user just gives the high -level goal, and
the agent has to figure out the entire step -by
-step plan itself. But basic planning often fails,
right? It does, because the AI tends to jump
the gun. You say... Plan a trip to Paris for
three days. And it immediately starts looking
for flights. Exactly. Flights, hotels. It skips
the crucial first step, asking questions. What's
the budget? When are you going? Who's even traveling?
It just defaults to a generic plan. Right. Minimum
effort solution. Yeah. So the better structure,
often called React, reasoning and action. It
forces a mandatory plan and critique cycle. Oh,
cycle. Yeah. A rigid four -step process it must
follow before it does anything. OK, what are
the steps? Thought. Clearly define the user's
ultimate goal. Step two. Initial plan. Write
down the steps it thinks it needs. One. Two.
Three. Makes sense. This is the core self -critique.
The AI must ask itself. What information am I
missing? Budget. Dates. Interests. Where could
this plan go wrong? This is where it confronts
its own ignorance. It forces it to see the gaps.
Exactly. Then step four. Final plan. It takes
the critique and fixes the plan. And here's the
key. The very first step in that final plan,
almost always it should be using that ask user
for info tool we talked about. Ah, connecting
back to tool use. Yep. This whole structure stops
the AI from just rushing ahead vaguely. It makes
sure it gets the necessary info first before
wasting time or, you know, computing resources.
So that mandatory self -critique phase forces
it to gather info first. That one step prevents
the rush to a bad generic solution. It builds
quality in right from the start. Okay, that makes
a lot of sense. We need to take a quick break.
When we come back, we'll dive into the final
most advanced recipe, multi -agent workflows.
Sounds good. Mid -roll sponsor read content to
be inserted by Rider. All right, we're back.
We've covered reflection, tool use, and planning.
Now for the final recipe, which you said is the
most advanced, multi -agent workflow. Yeah, this
is where we stop trying to make one single general
AI do everything perfectly. Instead, we build
a team. A team of specialized AI agents. OK,
because the normal way, that's usually just us,
right? Doing the glue work. Exactly. Manual glue
work. You ask your research AI for market trends.
You copy the text. You paste it into your writing
AI to generate some ad copy. And you're the one
connecting the dots, trying to keep the context
straight between copy paste. Right. And context
gets lost or muddied. It's inefficient. The pro
approach is setting up clear specialized roles
for each agent and really strict rules for how
they hand off. information. Think of it like
a hyper -specialized assembly line. An assembly
line for AI. Pretty much. The key concept here
is fighting something called function creep.
Function creep? What's that? It's when a single
agent starts getting asked to do jobs it wasn't
really designed or optimized for. Its core quality
slowly degrades because it's being stretched
too thin. Specialization prevents that. Okay,
let's use that ad campaign example you mentioned.
So instead of one AI, we have three. Yeah, let's
say three distinct roles. Agent one. We'll call
him Data Dave. Data Dave. OK. His job is purely
logical, factual, analyze market trends. And
crucially, his output must be a very strict JSON
object. Maybe detailing target audience, key
trends, the core pain point. He only speaks data,
no creativity allowed. Strict JSON. Got it. Then
that JSON goes to? Creative Carla. Her role is
all about emotion narrative. turning Dave's dry
data into, say, three compelling ad options.
Yeah, because she gets that strict JSON contract
from Dave. She does exactly which pain points
she needs to address. No guesswork. OK, makes
sense. And agent three. Manager Mike. His role
is practical, results focused. He takes Carla's
creative ads, compares them against Dave's original
data analysis. Checks if they actually match
the research. Right. And then he picks the single
best ad and explains why based on the data and
the goal. OK, but. Couldn't you just use one
really big powerful model like GPT -4 for all
of this? Seems way less complicated than setting
up and managing this whole AI team. It might
seem simpler up front, yeah, but the quality
difference can be huge. That specialization is
key. Why though, if the big model is smart enough?
Because of that function creep we talked about.
Data Dave cannot write good ad copy. Creative
Carla cannot do rigorous data analysis. Forcing
them into specialized lanes maintains peak performance
for their specific task. And the other crucial
piece, that strict JSON format for handoffs,
it acts like an unbreakable contract between
them. It drastically reduces the risk of hallucination
or misinterpretation as information moves down
the line. Context is preserved perfectly. The
quality boost comes from forcing specialization
and using that strict data contract. between
them. Exactly. Specialization stops function
creep. Strict JSON ensures perfect context transfer.
You get a better final product from the assembly
line. Okay, so let's recap the big ideas. Four
recipes for moving beyond basic prompts. Yep.
First, reflection improving internal quality
using a strict rubric for self -critique. Second,
tool use giving the AI external capabilities
but guiding it with few -shot examples so it
knows when and how. Third, planning creating
autonomy with that mandatory self -critique step
to force information gathering before acting,
using structures like React. And fourth, multi
-agent building specialized teams that communicate
via strict formats like JSON to maximize quality
and avoid function creed. That's the progression.
From simple internal checks to complex orchestrated
teams. Now, you probably shouldn't try to build
a complex multi -agent system tomorrow. Definitely
not. My advice is start simple. Get comfortable
with the earlier recipes first. Right. So here's
some actionable homework for you, the listener.
Try recipe one, reflection, today. Yeah, pick
a simple task you do often, maybe writing email
subject lines. OK. Ask your AI to write, say,
three subject lines for an email. Then immediately
after, give it a rubric. Tell it. Grade these
subject lines from one to five on curiosity and
one to five on urgency make it be strict and
then the final step Demand new subject lines
tell it now write three new ones that score five
out of five on both curiosity and Urgency based
on your own critique and just notice the difference
between that first attempt and the refined reflected
version you can be dramatic it really shows that
if Just changing a prompt slightly, adding that
reflection step can give you such a better result.
Imagine the power when you start layering these
patterns, tool use on top of reflection, planning,
managing tool use. It compounds. So here's a
final thought to leave you with, something to
chew on. OK. If your AI agent, the one you use
every day, if it always checked what critical
information it was missing before it took any
action. How would that one change, that architectural
shift based on the planning recipe, how would
that fundamentally change your daily workflow,
your decision -making? That's a really interesting
question to consider. How much guesswork would
that eliminate? Something to think about. Definitely.
A thought to carry with you until our next deep
dive. Thanks for tuning in.