Data Preparation

Training LLMs: Data Preparation 🍳

The Kitchen Analogy

Imagine you’re teaching a robot chef to cook the world’s best dishes. But here’s the thing—this robot has never tasted food, never been to a grocery store, and doesn’t know the difference between salt and sugar.

How do you teach it?

You give it recipes (data). Lots and lots of recipes. But not just any recipes—the right recipes, prepared the right way.

Training an LLM (Large Language Model) is exactly like this. The AI is your robot chef, and the data you feed it is the recipe book that shapes everything it learns.

Let’s explore the five secret ingredients to preparing perfect training data!

1. Dataset Preparation 📦

What Is It?

Dataset preparation is gathering and organizing all the recipes your robot chef will learn from.

Think of it like this: Before you can teach someone to cook, you need to:

• Collect cookbooks from around the world
• Remove duplicate recipes
• Throw away recipes written in languages you can’t read
• Organize them by category (appetizers, mains, desserts)

Why Does It Matter?

If you give your robot chef a messy pile of torn pages, coffee-stained notes, and recipes in 47 different languages—it’s going to learn chaos.

But if you give it a clean, organized cookbook? Magic happens.

The Process

graph TD
    A["🌐 Collect Raw Data"] --> B["🧹 Clean the Data"]
    B --> C["🏷️ Label & Categorize"]
    C --> D["✂️ Split into Train/Test"]
    D --> E["✅ Ready for Training!"]

Simple Example

Bad Dataset:

• “How 2 make cake???”
• “BEST CAKE RECIPE CLICK HERE!!!”
• “ケーキの作り方” (mixed languages)
• “The cake is a lie” (not a recipe)

Good Dataset:

• “Classic Vanilla Cake: Preheat oven to 350°F…”
• “Chocolate Layer Cake: Mix 2 cups flour…”
• “Red Velvet Cake: Combine cocoa powder…”

Key Actions

• Collect from diverse, quality sources
• Clean by removing junk, duplicates, broken text
• Format consistently (same structure everywhere)
• Split into training set (90%) and test set (10%)

2. Data Augmentation 🔄

What Is It?

Data augmentation is like teaching your robot chef the same recipe in 100 different ways.

Imagine you only have one recipe for chocolate cake. That’s not enough! But what if you:

• Rewrote it with different words
• Changed “2 cups flour” to “500 grams flour”
• Added slight variations (“add a pinch of sea salt”)

Now you have 100 recipes that teach the same thing—but the robot learns it deeply.

Why Does It Matter?

More variety = Better learning.

If a child only sees one picture of a dog (a golden retriever), they might think only golden retrievers are dogs. But show them 100 different dogs? Now they understand what a dog really is.

Augmentation Techniques

graph TD
    A["🍰 Original Recipe"] --> B["📝 Paraphrase It"]
    A --> C["🔀 Shuffle Sentences"]
    A --> D["🌍 Translate & Back-Translate"]
    A --> E["➕ Add Slight Variations"]
    B --> F["🎯 10x More Training Data!"]
    C --> F
    D --> F
    E --> F

Simple Example

Original:

“To make pancakes, mix flour and eggs.”

Augmented Versions:

1. “Combine flour with eggs to prepare pancakes.”
2. “Pancakes are made by mixing eggs and flour together.”
3. “Mix eggs into flour—that’s how you start pancakes.”
4. “For pancakes: flour + eggs, mix well.”

Same meaning. Different words. Richer learning.

Popular Techniques

• Synonym replacement: Swap words with similar meanings
• Back-translation: Translate to French, then back to English
• Random insertion: Add related words naturally
• Sentence shuffling: Reorder sentences (when order doesn’t matter)

3. Training Data Quality 🌟

What Is It?

Quality is making sure every recipe in your cookbook is actually good.

Would you want your robot chef learning from:

• Burnt recipes that didn’t work?
• Recipes missing half the ingredients?
• Recipes written by people who’ve never cooked?

Of course not! You want the best recipes from the best chefs.

Why Does It Matter?

“Garbage in, garbage out.”

If you train an AI on bad data, it learns bad habits. Train it on excellent data? It becomes excellent.

Quality Dimensions

Quality Checklist

graph TD
    A["📊 Raw Data"] --> B{Is it accurate?}
    B -->|No| X["❌ Remove"]
    B -->|Yes| C{Is it complete?}
    C -->|No| X
    C -->|Yes| D{Is it relevant?}
    D -->|No| X
    D -->|Yes| E{Is it consistent?}
    E -->|No| X
    E -->|Yes| F["✅ Keep!"]

Simple Example

Low Quality:

“Cake recipe: Put stuff in oven. Wait. Done.”

High Quality:

“Classic Vanilla Cake: Preheat oven to 350°F (175°C). In a large bowl, cream together 1 cup butter and 2 cups sugar until light and fluffy. Beat in 4 eggs, one at a time. Mix in 1 tsp vanilla extract. Combine 3 cups flour with 1 tsp baking powder…”

The difference? One teaches nothing. The other teaches everything.

4. Synthetic Data Generation 🤖

What Is It?

Synthetic data is creating brand new recipes that never existed before—using AI!

Sometimes you don’t have enough real recipes. Maybe you need:

• 10,000 recipes for rare cuisines
• Examples of dishes that are hard to find
• Practice data for edge cases

Solution? Make it up (intelligently).

Why Does It Matter?

Real data is:

• Expensive to collect
• Sometimes unavailable
• Often biased or incomplete

Synthetic data fills the gaps. It’s like having a master chef invent new recipes based on cooking principles.

How It Works

graph TD
    A["🧠 AI Generator"] --> B["📝 Generate New Data"]
    B --> C{Quality Check}
    C -->|Good| D["✅ Add to Dataset"]
    C -->|Bad| E["🗑️ Discard"]
    D --> F["🎯 Larger, Richer Dataset!"]

Simple Example

Real Data You Have:

• Italian pasta recipes (1,000)
• Japanese noodle recipes (50)

Problem: Not enough Japanese recipes!

Synthetic Solution: Use an AI that understands cooking to generate 950 more Japanese-style recipes based on patterns it learned.

Result: Balanced dataset with 1,000 examples each.

When to Use Synthetic Data

• Privacy concerns: Medical records can be synthesized, not real
• Rare cases: Unusual situations need examples too
• Balancing: Some categories have less data
• Cost: Real data is expensive to label

Caution!

Synthetic data must be:

• Validated by humans or other AI
• Mixed with real data (not 100% synthetic)
• Quality-checked like any other data

5. Checkpointing 💾

What Is It?

Checkpointing is saving your progress so you don’t lose everything.

Imagine you’re teaching your robot chef for 6 months. Then suddenly—POWER OUTAGE! If you didn’t save your progress, you’d have to start over from day one.

Checkpointing = hitting “SAVE” regularly during training.

Why Does It Matter?

Training LLMs takes:

• Days or weeks of computation
• Millions of dollars in resources
• Massive amounts of electricity

If something goes wrong without checkpoints? All that work vanishes.

How It Works

graph TD
    A["🚀 Start Training"] --> B["📈 Train for a while"]
    B --> C["💾 Save Checkpoint"]
    C --> D["📈 Keep Training"]
    D --> E["💾 Save Another Checkpoint"]
    E --> F{Problem?}
    F -->|Yes| G["⏪ Restore Last Checkpoint"]
    F -->|No| H["📈 Continue Training"]
    G --> D
    H --> E

Simple Example

Without Checkpoints:

• Train for 7 days
• Computer crashes on day 7
• All progress lost
• Start over from day 1 😭

With Checkpoints (every 6 hours):

• Train for 7 days
• Computer crashes on day 7
• Restore from last checkpoint (6 hours ago)
• Only lose 6 hours, not 7 days! 🎉

What Gets Saved?

A checkpoint includes:

• Model weights: All the numbers the AI learned
• Optimizer state: Training configuration
• Training step: Where you left off
• Metrics: Performance so far

Best Practices

• Save checkpoints regularly (every few hours)
• Keep multiple checkpoints (not just the latest)
• Store in reliable, backed-up storage
• Test that you can actually restore from them!

Putting It All Together 🎯

Here’s the complete data preparation pipeline:

graph TD
    A["🌐 Raw Data Collection"] --> B["📦 Dataset Preparation"]
    B --> C["🌟 Quality Checks"]
    C --> D["🔄 Data Augmentation"]
    D --> E["🤖 Synthetic Data if needed"]
    E --> F["🚀 Start Training"]
    F --> G["💾 Regular Checkpoints"]
    G --> H["🎉 Trained Model!"]

Quick Summary

The Big Picture 🌟

Training an LLM is like raising a genius child:

1. Give them great books (Dataset Preparation)
2. Explain things many ways (Data Augmentation)
3. Only teach correct information (Training Data Quality)
4. Create practice problems (Synthetic Data Generation)
5. Never forget what they learned (Checkpointing)

Do all five right, and your AI will be ready to create the impossible.

You’ve got this! 🚀