Build datasets & train LLMs that drive real business value

Learn how to create domain‐specific datasets for large language models, so you can build a true AI moat your competitors can't copy.

I've been saying that no AI generated content will break through the algo. 100% proved me wrong.

Anthony Pierri

/in/anthonypierri

This is by far the best LinkedIn AI generated content on the market.

Jordan Crawford

/in/jordancrawford

Since I started using the GrowGlad model I'm getting 2–3 inbounds a week. I used to get zero.

Zack Toyota

/in/zack-toyota

Why is my fine-tuned LLM performing worse than the base model?

Hi! I'm Jacob Warren, the creator of GrowGlad. Over the past year I've consulted CTOs, partnered with CEOs, and worked alongside ex-Meta ML engineers to answer one question:

Why my LinkedIn AI model is so good, and why their models spit out gibberish.

Since launching GrowGlad, I've talked to countless developers and CTOs who've invested time and resources into fine-tuning their models—only to find that their model's peformance actually deteriorated from the base model's baseline. They were confused, frustrated, and left wondering why their efforts weren't translating into better business results.

What Went Wrong?

False Promises of Fine-Tuning
Many assumed that tweaking a pre-trained model was as straightforward as putting data in the model, one chunk at a time. Instead, they discovered this approach backfires adn degrades the model.
Neglecting Dataset Quality
A model is only as good as the data it learns from. Relying solely on fine-tuning without building a high-quality, domain-specific dataset means the model never truly aligns with the business needs.
Overlooking Feature Engineering
The secret sauce isn't in the fine-tuning—it's in generating a dataset with the right features. Effective feature Engineering drives the model to generate outputs that are consistent, on-brand, and impactful to the products they power.

What You'll Learn in This Course

Build Competitive Moats
Follow every step I took to create datasets that set my model apart, ensuring your company becomes an AI-first leader.
Achieve Business-Level Impact
Learn actionable tactics that transform raw data into finely tuned features, giving your model the edge it needs to perform at the highest level.
Avoid Common Pitfalls
Discover why many fine-tuning efforts fail and how to steer clear of these mistakes to create a model that drives success.

Go from this...

llm@CoolApp~python prompt.py

Generate the JSON for a CloudFormation template that defines an AWS Lambda function named 'ProcessNewFiles', written in Python 3.9, triggered by an S3 'ObjectCreated' event. Include the required IAM role and any necessary permissions. Add comments for clarity.

{“response:” “art�e, r ”, “extraKey”: 23432}My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...My reason is that...[/INST]

To this... Every time.

llm@CoolApp~python prompt.py

Name: ProcessNewFiles
Bucket: example-bucket

{
  "AWSTemplateFormatVersion": "2010-09-09",
  "Resources": {
    "ProcessNewFilesFunction": {
      "Type": "AWS::Lambda::Function",
      "Properties": {
        "FunctionName": "ProcessNewFiles",
        "Runtime": "python3.9",
        "Handler": "index.handler",
        "Role": {
          "Fn::GetAtt": ["ProcessNewFilesRole", "Arn"]
        },
        "Code": {
          "ZipFile": "def handler(event, context):\n
          # lambda code\n pass"
        }
      }
    },
    "BucketNotificationConfiguration": {
      "Type": "AWS::S3::Bucket",
      "Properties": {
        "BucketName": "example-bucket",
        "NotificationConfiguration": {
        "LambdaConfigurations": [
        {

Why You Should Care

Venture Capital and Accelerator Cohorts
Y Combinator and other accelerators have witnessed a surge in generative AI applications. VC partners indicate that many pitch decks rely on the same large model APIs—pointing to a lack of technical differentiation.Sequoia Capital and Andreessen Horowitz (a16z) have even advised founders to focus on creating genuine moats through custom data. Coincidentally, just what you're going to learn in this course.
Demand for Domain-Specific LLM Engineers
Tech companies—both startups and established enterprises—are scrambling to hire developers who can build proprietary datasets and fine-tune models, not just prompt them. This skillset is rapidly becoming a top differentiator in AI-driven organizations. By mastering how to label data, identify key features, and tailor an LLM to a niche, you'll be better equipped to stand out in an increasingly competitive job market.
Market & Ecosystem Insights
Reports from CB Insights, PitchBook, and NFX note most new generative AI startups build on top of general-purpose LLMs rather than training from scratch. This underscores how building a proprietary dataset is a crucial competitive advantage. Simultaneously, OpenAI’s API usage stats and the explosion of open-source models (Llama, Falcon, StableLM, etc.) highlight how many projects still rely heavily on prompt engineering—reinforcing the need for deeper domain-specific data to truly differentiate your product or service.

What Constitutes a Real "AI Moat"?

Proprietary Data & Fine-Tuning
Owning a unique dataset—be it from specialized industry documents, medical records, or another exclusive source—creates a barrier that competitors can't easily overcome. We'll teach you how.
Engineering Features as User Levers
Developing features that leverage the unique data you have can significantly enhance performance and increase customer experience. By translating your unique first-party data into specific tools, controls, or "levers" in your app, you empower users to interact with information in ways a generic LLM or rival product can't match.
General-purpose LLMs work from broad, non-personalized data. Once you've embedded your domain's specialized, first-party data into tangible features, you build an experience only your product can deliver. Competitors lack the same domain insights, so they can't simply clone these feature "levers" overnight.
In other words, you turn your unique data into user-facing functionality—creating a defensible moat around your product's user experience.
Integration and Product Depth
Deep integration with your systems and sophisticated domain logic not only sets your product apart but also builds a lasting competitive advantage.

If you aspire to build a truly AI-first product—one that not only integrates AI but leads with it, attracts funding, and outpaces competitors—this course is for you. Let's move beyond ineffective fine-tuning and build the datasets that will power your success.

The Course

40+ in-depth video tutorials that walk you through how to build a dataset, extract important features, fine-tune a model, and iterate over it until it's ready to publish.

From theory to implementation, you'll learn the ins-and-outs of this iterative, domain-led way to build LLM datasets, starting with minimal features and scaling up complexity only when results demand it.

These approach is tried-and-true, consistently followed by many experienced data scientists and ML engineers.

However it's not well-documented and rarely applied to LLMs.

That's why I've spent over $200,000 learning this process myself over the years.

And now I'm making this course so you can learn to do the same thing the easy way.

Full Course Overview

Buy Now

Module 1: Introduction

Course Goals & Structure

Module 1: Introduction

The LinkedIn Model Example

Module 1: Introduction

Applying These Skills Elsewhere

Module 2: Foundational Concepts of LLMs

Transformer Architecture

Module 2: Foundational Concepts of LLMs

Pre-Training vs Fine-Tuning vs Alignment

Module 2: Foundational Concepts of LLMs

Identifying a Domain

Module 2: Foundational Concepts of LLMs

Why Datasets Differentiate

Module 3: Data Collection Strategies & Multi-Tier Engagement

Scraping & TOS Compliance

Module 3: Data Collection Strategies & Multi-Tier Engagement

Understanding Gradients

Module 3: Data Collection Strategies & Multi-Tier Engagement

Finding our Engagement Gradient

Module 3: Data Collection Strategies & Multi-Tier Engagement

Translating to Other Domains

Module 4: The Psychology of Feature Development

Why Features Matter

See all videos in course

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Learn how to create domain‐specific datasets for large language models, so you can build a true AI moat your competitors can't copy.

Early Bird Special!

The Essentials

$199$149

USDone time

The 40+ video online course
Small dataset to follow along

The Professional

$249$199

USDone time

The 40+ video online course
Small dataset to follow along
All feature engineering scripts
Extra videos on advanced fine-tuning approaches like GRPO

The Complete Package

$399$349

USDone time

The 40+ video online course
Small dataset to follow along
All feature engineering scripts
Extra videos on advanced fine-tuning approaches like GRPO
Complete raw and final datasets to completely reproduce model

Frequently asked questions

Build datasets & train LLMs that drive real business value

Why is my fine-tuned LLM performing worse than the base model?

What Went Wrong?

False Promises of Fine-Tuning

Neglecting Dataset Quality

Overlooking Feature Engineering

What You'll Learn in This Course

Build Competitive Moats

Achieve Business-Level Impact

Avoid Common Pitfalls

Go from this...

To this... Every time.

Why You Should Care

Venture Capital and Accelerator Cohorts

Demand for Domain-Specific LLM Engineers

Market & Ecosystem Insights

What Constitutes a Real "AI Moat"?

Proprietary Data & Fine-Tuning

Engineering Features as User Levers

Integration and Product Depth