You've likely experienced this. You spend hours,
maybe days, carefully prepping documents, chunking
them just right, loading them into your vector
database. Then you ask your shiny new AI agent
a pretty simple, direct question. And you watch,
maybe with that sinking feeling, as it confidently
spits back an answer that's, well, completely
irrelevant. Or maybe just plain wrong. That specific
moment of dismay when you're our agent just completely
misses the point. Yes, that feeling. We've absolutely
all been there. It's so frustrating. Welcome
to the Deep Dive. Today, we're really going to
dig into that exact challenge. It's pretty widespread
in AI development, actually. How do we turn those
frustrating, sometimes scattershot information
retrievers into real precision knowledge machines?
Precisely. Yeah, it's all about fixing arguably
the number one flaw in retrieval augmented generation
agents, our edge agents. We're going to unpack.
two really powerful techniques, re -ranking and
metadata enrichment. Think of them like superpowers
for your AI. We'll explain why your ag agents
might be failing you, and then, importantly,
give you a practical blueprint to fix them. We'll
show how tools like, say, NAN and Supabase make
this doable. We'll even walk through a specific
example using, of all things, golf rules. So
what does this all really mean for building AI
you can genuinely rely on? OK, let's get into
it. Let's start maybe with the basic flow, the
fundamental steps in most standard RG setups.
You have your source document, right? A PDF,
maybe something else. It gets broken down into
smaller bits, chunks, as they're called. Right.
And each of those little chunks gets turned into
a, well, a numerical vector by an AI model. It's
kind of like a mathematical fingerprint of what
that chunk means. These fingerprints, these vectors,
they get stored in a special kind of database,
a vector database. Then when a user asks a question.
The question itself gets turned into a vector
too. The system hunts through the database for
the vectors that are mathematically closest.
That's the nearest neighbor search part. The
text chunks linked to those closest vectors get
pulled out and fed to a large language model,
an LLM, which then writes the final answer. It
sounds pretty logical, doesn't it? It does sound
logical on paper, but here's the real kicker,
the critical point where it often breaks down.
It's the assumption that mathematically similar
always means contextually relevant. That's the
flaw. It's like, imagine asking about... payment
terms for a service, and the system brings back
stuff about terminating a contract. The words
might overlap a bit, terms, terminating, but
the actual meaning, the context, completely off.
And that's why a lot of promising AI projects
just kind of fizzle out. They don't deliver reliable
answers. Okay, so what's that core assumption
basic argue systems make that often leads straight
to irrelevant answers? Basically, it assumes
mathematical similarity equals true relevance.
And that's just not always true. Right. And this
is precisely where re -ranking comes into play.
It feels like a game changer. How exactly does
it tackle that core problem you just laid out?
Okay, think of re -ranking like adding a super
smart quality control manager to your information
assembly line instead of just grabbing the first
few things off the belt that look roughly right.
It takes a much bigger batch, like a whole pile
of potential candidates, and then it carefully
inspects each one for how well it actually fits
the original request. Okay, so you cast a wider
net initially, but then you apply a much more
discerning filter. Can you maybe contrast the
old way versus this new re -ranked flow? Absolutely.
So the traditional RU flow, user asks a question.
The system grabs maybe the top three or four
nearest vectors based purely on math, sends those
straight to the AI. It's often a bit of a hope
and pray method, honestly. You just hope those
top few are good enough. But with the re -ranked
flow, the vector search still happens first,
but you tell it, hey, bring me back more options,
maybe 10, 20, even more candidate vectors, a
wider net. Then this bigger pool of candidate
chunks gets passed to a specialized re -ranker
model. And what's really interesting here is
that the re -ranker's only job is to look at
that bigger set of chunks and compare each one
directly against the original user query. It's
not just doing math similarity again. It's looking
for genuine contextual relevance. It assigns
a score like 0 .0 to 1 .0 saying, how relevant
is this really? And then crucially, it throws
away everything except the very top scores, maybe
the best three or four. Those are what finally
go to the LLM. It massively boosts the quality
of the input for the AI. Yeah, it ensures the
AI gets only the most relevant stuff, makes the
final answer way, way more accurate. And, you
know, this isn't just theory. It's actually surprisingly
straightforward to put into practice, especially
using tools like NAN that handle a lot of the
complexity. Right. The main thing you need first
is just a basic arg workflow already set up.
Maybe you're already using something like Supabase
as your vector database. So let's walk through
the actual steps to add Cohere re -ranking. First
up, you need an API key from Cohere. You go to
Cohere .com, sign up. They have a pretty generous
free tier, which is great for getting started.
Grab an API key. Maybe name it something clear
like an 8 -ranker key. Okay. Step one, get the
key. Then what? Then you need your vector database
set up. Let's stick with Supabase. You'll create
a table, let's call it documents, to hold your
chunks. It needs a few columns, like an id, maybe
an integer, the actual content as text, a metadata
column, probably JSON B format for flexibility,
and of course the embedding column itself, which
holds the vector data type. Got it, table structure.
Then connect it in ANAN. Exactly. You go to your
Supabase vector store node in your NAD workflow.
Now, this bit's important. You need to find the
limit setting. By default, it might be set low,
like four or five. You have to crank that up,
set it to maybe 20. That's casting the wider
net we talked about. Ah, okay. Increase the limit
first. Yes. And then you just flick the switch.
There's usually a toggle labeled re -rank results.
Enabling that makes new fields appear for the
re -ranker setup. Makes sense. And the final
step. just configure those new fields you'd select
cohere as the provider paste in that api key
you got earlier and pick a re -ranker model re
-rank v3 .5 is a good one save it and you've
basically integrated re -ranking okay that does
sound pretty doable but it brings up an important
question Why is it so critical to increase that
initial limit in the vector store node before
the re -ranking happens? Why not just re -rank
the default four or five? Because the re -ranker
needs a decent pool of candidates to show its
value. If you only give it four options, maybe
none of them are actually that relevant. By giving
it 20, you significantly increase the odds that
some truly relevant chunks are in that initial
pool for it to find and promote. It needs options
to work its magic. Okay, so before a RAG agent
can even start answering questions with re -ranking,
we need to actually feed it knowledge. build
its brain so to speak that happens to a data
preparation workflow right this is the back end
pipeline it's what takes your source document
like that pdf of golf rules processes it smartly
and loads it into super base ready for the agent
to use this is how you build the actual knowledge
base so the process might look something like
this first you get your source pdf maybe download
it then you extract the raw text out of it and
here's a crucial part structuring that raw text.
You could use, for instance, a JavaScript code
node in 8AN. You might write a little script
or even get AI to help write it. Oh, interesting.
A script that looks for patterns, like rule number
X in the text, and uses those patterns to split
the document into meaningful JSON objects. Each
object might have, say, a view number, a rule
title, and the full text for that specific rule.
Ah, so you're breaking it down logically, not
just by random character counts, and adding structure
right away. Then, presumably, you load that structured
data. Exactly. You load that structured data,
and importantly, you add metadata during this
step, like that rule number you just extracted,
maybe a document type like official rules, maybe
a date created. Then you generate the vector
embeddings for these nice, structured, metadata
-tagged chunks. You could use an open AI model
like text embedding 3 small with your open AI
key. And finally, you upload the whole package,
the structured text, the metadata, the embeddings
into your Supabase table. This careful prep work
is really foundational. Absolutely. If you think
about the big picture, the quality and thoughtfulness
you put into this data preparation stage, that
directly dictates how reliable and accurate your
entire RAG system will be later on. It's the
bedrock. Okay, so why is strategic data preparation
considered the foundational step for a good RAG
agent? Because good data prep ensures you have
a structured, accurate knowledge base. That's
absolutely critical for the AI's performance
down the line. Let's make this concrete. Let's
talk about that golf rules agent again. It was
built from a 22 -page PDF covering 28 distinct
golf rules. Right. So the test query we used
was, how is the order of play determined in golf?
Simple enough question. Now, without re -ranking,
a basic argue system, just looking at word similarity,
it might easily grab chunks talking about penalties
or maybe equipment rules. Why? Because the word
play shows up in those contexts, too. That leads
to irrelevant bits getting mixed in, and you
end up with a confusing, maybe incomplete answer.
Okay. But then we switched on re -ranking. The
Supabase node first did its job, fetching 20
chunks that were mathematically similar to the
query casting that wide net. Then the Cohere
re -ranker stepped in and evaluated each of those
20 chunks for true relevance to the question
about order of play. And the results were really
clear. Chunk 1, which talked about match play
order, got a high score. 0 .877 super relevant
chunk two explaining stroke play order also scored
well 0 .642 still very relevant chunk three was
about provisional balls less directly relevant
but somewhat related scored 0 .57 but crucially
the other 17 chunks they had much lower relevant
scores the re -ranker basically said nope these
aren't really about the order of play and discarded
them Only the top ones went forward. And the
AI's final answer, built only from those top
-scoring, truly relevant chunks. It was perfect.
It was comprehensive, accurate, detailing both
match play and stroke play order. It even offered
more details if needed. The re -ranker acted
like this incredibly precise filter, cutting
out all the noise. Yeah, and what's really cool,
especially in a tool like NEM, is the transparency.
You can actually look at the logs and see it
happen. You see the query. You see the initial
20 results pulled. You see the re -ranker scores
for each one. And you see exactly... which top
chunks got selected to build the answer makes
troubleshooting and optimizing so much clearer.
It's really powerful to see. Makes you think,
whoa, imagine applying that same level of precision,
that same transparent filtering to something
massive like a billion complex scientific papers.
The potential is just huge. So what was the key
difference the re -ranker made in the golf agent's
answer? It precisely filtered for true relevance.
which directly resulted in a comprehensive and
accurate answer. Okay, so re -ranking boosts
relevance significantly. But you mentioned metadata
earlier, saying it brings surgical precision.
What exactly does that mean in this context?
Right, surgical precision. It tackles a different
but related problem, what we call the chunk -based
retrieval problem. Think about it. A single logical
piece of information, like golf rule three on
stroke play, might actually get split across
several different text chunks when you do the
initial document processing. It just happens
sometimes based on length or structure. Now without
metadata, if you ask the agent, tell me everything
about rule three, the system just does its usual
vector search based on the words in your question.
It might find some chunks related to rule three,
but it could easily miss other crucial parts
of that same rule. If they happen to land in
chunks that don't seem mathematically similar
enough to your question, you get fragmented information.
Ah, I see. The information is in the database,
just scattered across chunks that the basic similarity
search might miss. So how does tagging every
chunk with structured info, like a simple rule
number, three tag, fix that fragmentation? It's
simple, but powerful. By adding that rule number,
three, tag to every chunk that contains any part
of rule three, you give the system another way
to find information. Now, instead of just relying
on semantic similarity to the question, the system
can be told, hey, retrieve all chunks that have
the metadata tag rule number three. Boom. It
instantly pulls together the complete context
for rule three, no matter how the text was chunked
or how similar the individual chunk content is
to the query itself. It ensures you get the whole
picture. It's like giving every piece of info
a precise address. Okay. That makes a lot of
sense. So how do we actually add and then use
this metadata effectively, say, with an N8N you
said sort of two -step thing? Yeah, two main
parts. Step one is adding the metadata during
that data preparation phase we talked about earlier.
When you're processing your documents and getting
them ready for the Vexor database in your data
loader node, you explicitly add these key value
pairs. So along with the text content, you'd
add fields like rule number, one, document type,
official rules, date created, 2024 -01 -15, whatever
makes sense. for your data. That metadata gets
stored right alongside the text chunk and it's
embedding. Got it. Added during prep. And step
two, you mentioned a smart way to extract it.
Right. The smart way avoids manual labor. Instead
of manually figuring out which rule is which,
you can leverage AI itself. Take the raw text
from your golf PDF, for example. You could go
to an AI chat tool like ChatJPT or Claude and
give it a prompt like, Hey, help me write some
JavaScript code for an N8n code node. This code
needs to take this big block of text as input
and split it into separate items every time it
finds the pattern rule, followed by a number
and a colon, like rule X. For each item, extract
the rule number and the rule title. The AI can
often generate perfectly usable code for you.
You paste that code into an N8N code node, and
boom, it automatically parses your document into
structured items, each already tagged with its
rule number and title. Super efficient. Wow,
using AI to help structure the data for the AI.
Clever. Exactly. And then you can get even more
advanced with dynamic metadata filtering. Imagine
this. A user asks, tell me about rule eight.
You could have a first AI agent whose only job
is to analyze that query. it recognizes the user
wants a specific rule and outputs just the metadata
filter needed rule number 80 then your main og
agent uses that specific filter in its super
base query maybe alongside the vector search
or maybe even instead of it for such a direct
query that's surgical precision targeting exactly
the data you need based on the query's intent
okay so the smart way to add metadata involves
using AI to generate code that automatically
extracts and tags that structure data from your
documents. And the potential uses for this kind
of metadata filtering, they go way, way beyond
just golf rules. Honestly, it's like a superpower
for almost any business data you can think of.
Yeah, I could see that. Give us some examples.
How would this apply in, say, a business context?
Okay, imagine you record and transcribe all your
team meetings. You could add metadata like .date,
2024, Z620, participants, John, Sarah, project
.website, or design. Then you can ask your AI
agent, what did we decide about the homepage
navigation during the website redesign meeting
Sarah attended in June? The metadata makes that
possible. Or for legal documents. Yep. Client
contracts could have metadata like client name,
Acme Corp, document type, MSA, status, active,
renewal date, 2025 -03 -01. Imagine querying.
Show me all active master service agreements
for Acme Court that are up for renewal in the
next six months. Super targeted. And maybe one
more like for technical docs. Sure. A technical
knowledge base could use tags like topic, API
authentication, language, Python, library version
2 .1, difficulty advanced. Then a developer could
ask, find advanced Python examples for API authentication
using library version 2 .1 or later. Wow. Okay.
When you step back and look at it like that,
metadata really does fundamentally change how
you interact with huge piles of unstructured
text. It lets you query it almost like a structured
database. It's a total game changer. It unlocks
that database -like precision for messy, unstructured
data. So what kind of really new search capabilities
does metadata filtering unlock for businesses?
it enables these highly specific almost multi
-dimensional queries across large diverse data
sets that were previously just impossible okay
so if we think about building one of these really
robust rag systems not just a basic one but one
with re -ranking and metadata thinking about
it as a whole project we probably break it down
into maybe three distinct phases. Yeah, I think
that makes sense. Phase one has got to be data
preparation. And we can't emphasize this enough.
This phase is critical. It covers everything
from getting the documents in ingestion to chunking
them strategically, ideally along logical breaks
in the content, not just random character limits.
Then the AI -assisted metadata extraction we
talked about, storing it all neatly structured
in your vector DB. And vitally, quality validation.
You have to check that the chunks and metadata
are accurate before you build on top of them.
Garbage in. garbage out, right? Garbage in, garbage
out. Okay. Phase one, solid data prep. What's
phase two? Phase two is what I call intelligent
retrieval. This is the main workflow the user
actually interacts with. It starts with analyzing
the user's query, then potentially applying those
dynamic metadata filters if the query calls for
it. Next, doing the vector search, but retrieving
that larger pool of candidates, remember, like
20. Followed immediately by re -ranking to score
those candidates for true relevance. And finally,
feeding only the best, highest scoring chunks
to the LLM to generate the actual response. Okay,
prep, then intelligent retrieval. What's the
third phase? Is it ever really done? Huh, good
question. No, it's never truly done. So phase
three is continuous improvement. A good RG system
needs ongoing care and feeding. This means monitoring
things like the relevant scores the re -ranker
is producing. Are they consistently high? Or
are there queries where it struggles? Analyzing
the kinds of questions users are asking, maybe
that gives you ideas for new, useful metadata
tags to add. And definitely integrating user
feedback. Even a simple thumbs up, thumbs down,
was this answer helpful? On the responses can
help you pinpoint areas that need refinement.
I still wrestle with prompt drift myself sometimes,
getting the LLM prompts just right. So this continuous
improvement loop, it really is vital for making
sure the system stays effective long term. That
makes perfect sense. So thinking back to phase
one, data preparation, what would you say is
the single most critical aspect there for the
whole system's success? Quality validation, hands
down. Ensuring the accuracy of both the text
chunks and the metadata you extract is absolutely
paramount. Everything else relies on that foundation
being solid. As people start building these more
advanced R -rank systems using re -ranking and
metadata, are there common mistakes or pitfalls
they should watch out for? Oh, definitely. There
are a few classic traps people fall into. Knowing
them up front can save a lot of pain later. Okay,
let's hear them. First big one, over -engineering
metadata. You get excited about tags and create
like... 50 different metadata fields right at
the start, but most of them never actually get
used in queries. The fix. Start simple. Identify
maybe just two or three really key fields based
on how you think people will query. Add more
only when you see a clear need based on actual
usage patterns. Don't boil the ocean initially.
Okay, start minimal with metadata. What else?
Insufficient re -ranking candidates. This is
running the re -ranker, but only giving it the
default four or five results from the initial
vector search. Remember, the re -ranker needs
options. You've got to increase that initial
retrieval limit in your vector store node. Get
it up to at least 15 or 20. Give it a good pool
to choose from. Right. Feed the re -ranker properly.
Any others? Yeah, related to that. Ignoring relevant
scores. Just because the re -ranker put something
at the top doesn't mean it's actually good enough
if its score is really low. Don't just blindly
pass the top three results to the LLM. Implement
a score threshold. Say, only use chunks with
a re -ranker score of 0 .5 or higher. Filter
out the low confidence results, even if they're
ranked highest. That's a smart refinement. Setting
a quality bar. And the last one. Static metadata
schemas. This is defining your metadata structure
once at the beginning and then never revisiting
it. Your data sources might change. The kinds
of questions users ask will definitely evolve.
Your metadata schema needs to be treated like
a living document. Review it periodically, adapt
it, add new fields, maybe retire old ones. Keep
it relevant to how the system is actually being
used. Okay, those are great points. Why is it
so crucial, do you think, to actively avoid these
specific pitfalls when you're trying to build
a ROG system that people can actually trust?
It really comes down to engineering user trust.
Every time the AI gives an irrelevant or nonsensical
answer because of one of these issues, that trust
erodes. Avoiding these pitfalls is about building
a system that is consistently reliable and useful,
which is the only way users will keep coming
back to it. Makes sense. So looking at those
pitfalls, which one do you think is maybe the
most damaging to our ag systems' ability to adapt
and stay useful over the long term? Ooh, good
question. I'd probably say static metadata schemas,
because your data and user needs will change.
If your metadata structure can adapt, the system's
ability to precisely retrieve relevant information
will degrade over time, no matter how good the
other components are. Adaptability is key. So
let's bring it all together. What does this really
mean for you, the learner, trying to build better
AI? We know basic rag agents, while exciting,
can often feel... a bit clumsy, a bit hit or
miss. But what we've seen today is that with
intelligent design choices, they can transform
into something much more powerful. Exactly. It's
that combination, casting a wider net initially,
retrieving maybe 10 or 20 candidates, then applying
that smart, intelligent re -ranking to score
them for true contextual relevance, and then
layering on strategic metadata, filtering for
that surgical precision when needed, that synergy.
That's what creates a RRG system users can actually
trust, rely on, and get consistently accurate
answers from. It feels like a shift from just
sort of hoping the AI gets it right to actively
engineering this system so that it's much more
likely to get it right. These techniques re -ranking
smart metadata, they offer immediate, tangible
ways to improve your AI agent's accuracy and
overall usefulness. Yeah. Your RG agents basically
just got superpowers. Seriously. It's time to
move past the frustration phase and start empowering
your AI to really perform at its best. And for
you, the listener, this provides a clearer path
forward. a path towards ai interactions that
are deeply informed highly accurate and capable
of cutting through the sheer volume of information
we all face so here's a thought to leave you
with think about the data you interact with every
single day your emails your documents transcripts
articles what structured insights are currently
just hidden inside all that unstructured text
waiting Waiting for intelligent metadata to unlock
their real power, their true utility for you.
We really hope this deep dive has given you not
just a clearer understanding of these concepts,
but also a practical blueprint you can use to
start building more intelligent, more reliable
AI systems yourself. Until next time, go to your
own music.