JR's talk will cover strategies for connecting large language models with data from disparate sources like GA4, Search Console, Persona and Jobs to be Done data to foster high-level understanding of your website, visitors, and industry. He will cover things like: - Taxonomies from 100s-of-thousands of keywords. - Persona and JTBD assignment at scale - Mining GA4 data for new personas - Content generation at scale with novel ideas

1. CONFIDENTIAL
Feeding the Machine
… Learning
JR Oakes
LOCOMOTIVE AGENCY
Speakerdeck.com/jroakes
@collard.green (Bluesky)

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About Me
➔ VP, Strategy at LOCOMOTIVE.
➔ I share a lot of code on Github.
➔ I have a wonderful wife and two daughters.
➔ I write for SEL, SEW, and a few other publications.
➔ I live in Raleigh, NC and love meeting up with other SEOs.
➔ I love dogs.
➔ Corinne Bailey Rae is my mood music.
➔ I, technically, play bass.
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Ideas covered
Limitations of LLMs
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Strategies for Working
with LLMs
Content Use Cases

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Its Superpowers & Limitations
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Large Language Models

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LLMs are incredibly complex but beautiful
Source

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Bridging minds & machines

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LLMs are amazing!
Source

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Its Superpowers & Limitations
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Large Language Models

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LLMs make s*#t up.
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Limitation 1

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LLMs are trained to be helpful &
provide good responses.
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There is a lot going on behind the scenes
Source

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…But they get it wrong
LLMs want to give
you a ‘good’
answer, whether
the answer is
correct or not.
Source

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…But maybe they are getting slightly better?
Source

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LLMs can give different answers each
time you ask them the same question.
Not ideal when your workflow requires a precision.
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Limitation 2

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LLMs are non-deterministic
It’s all probabilities, baby!
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LLMs are non-deterministic
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Same prompt,
same model

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LLMs have limited context abilities.
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Limitation 3

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LLMs work well with good context
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“Kathy is an adventurous girl. She
has many friends”
Neural Coreference

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LLMs work well with good context
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“In 2018 there was a new bar that opened in downtown
Raleigh that served the most amazing croissants we have
ever eaten. Run by head chef, Reggie Lapou, this place has
grown into a landmark. This restaurant is called
__________.”
Ability to pay attention to prior information to utilize in the
prediction of the next word.

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8k, 16k, and 32k is really not that much
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LLMs don’t do recent.
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Limitation 4

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LLMs have limited knowledge
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How to Work with These
Limitations
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Solutions

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Retrieval-augmented generation
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Source

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Retrieval-augmented generation
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→ Guide for further reading
→ Quick Prototyping

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Retrieval-augmented generation
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Compression
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Goal of compression
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Input:
● types of ehr, 230
● ehr types, 220
● types of ehr software, 90
● types of ehr systems, 70
● … 200 more keywords
Output:
Understanding different types of
Electronic Health Record (EHR)
systems.
Total Search Volume: XX,XXX
Shorter related data → More context to LLMs

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Garbage in, Garbage Out
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Refine Your Data

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Preprocessing of data is needed to
transform the raw data we receive from
GA4, Search Console, and other tools.
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From raw data to refined insights
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Use Cases
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Data in Action

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Ecommerce
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● Collect positive &
negative reviews.
● Provide data to LLM.
● Determine Pros & Cons
for PLPs.

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Database site
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Utilize owned data…

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Database site
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Utilize owned data… and turn it into content gold.

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Data aggregator
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● Align Keywords +
content sample.
● Provide to LLM.
● Batch labels for manual
review.

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LLM & Data in Action
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Compression Pipeline
Example #1
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Decoding Data to Discover Conversion Trails

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GA4 data - before processing
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Process into specific page visits
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User journey
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Find common user paths
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Group path intents together
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Drill down to converting sequences
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Converting data
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https://github.com/jroakes/
Npath.git

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Hypotheses to test
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Compression Pipeline
Example #2
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Orchestrating Order in a Sea of Search Terms

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Keywords are messy
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Example: massage therapist duck

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Keywords are messy
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Example: massage therapist duck
We have a town in North Carolina named Duck.
Makes a lot more sense?

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Keyword subjects are clear
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Large Language Models allow us to
compress information while building
context.

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Keywords into taxonomy
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We can use different compression
strategies which allow us to give LLMs
data that is rich in context and get
fantastic results.

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Keywords into taxonomy
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Keywords into taxonomy
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Try it with your own data using our
open-source code.
Open source TaxonomyML

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What else can we do?
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Personas, Jobs to Be Done, and other
relevant groupings.

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What else can we do?
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Keyword queries to URL labelings process
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Persona Prompt
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Jobs to be done Prompt
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● Can be provided via API
and automated via
Python.
● Customize to be used
across various companies
and industries.

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How Do We Operationalize
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Process

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Easy & modifiable process
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Topic generation process
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Thank you for listening
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End