AI Principles

The science behind AI — transformer architectures, training dynamics, and evaluation methodology. MONA explains how AI actually works, with precision over hype.

Evaluation & Benchmarking Data & Datasets LLM Foundations RAG & Semantic Search Model Architectures
Diagram of one language model scoring another's output using pointwise, pairwise, and rubric-based grading modes
MONA explainer 10 min

What Is LLM-as-a-Judge and How One Model Scores Another's Outputs

What Is LLM-as-a-Judge and How One Model Scores Another’s Outputs ELI5

The measurement scaffolding behind a trustworthy LLM judge: ground truth, rubric, agreement metrics, and a human baseline
MONA explainer 10 min

Prerequisites for LLM-as-a-Judge: Eval Metrics, Rubrics, and Human Baselines

Prerequisites for LLM-as-a-Judge: Eval Metrics, Rubrics, and Human Baselines ELI5

How an LLM judge's verdict flips when two answers swap positions, and the three main judging biases
MONA explainer 10 min

Position Bias, Self-Preference, and the Technical Limits of LLM-as-a-Judge

Position Bias, Self-Preference, and the Technical Limits of LLM-as-a-Judge ELI5

Benchmark datasets GLUE, MMLU, and SWE-bench scoring and ranking large language models on a leaderboard
MONA explainer 10 min

What Are Benchmark Datasets and How GLUE, MMLU, and SWE-bench Measure LLM Performance

What Are Benchmark Datasets and How GLUE, MMLU, and SWE-bench Measure LLM Performance ELI5

Three failure modes of AI benchmarks: saturation ceilings, training-data contamination, and construct validity gaps
MONA explainer 9 min

Saturation, Contamination, and Construct Validity: The Technical Limits of AI Benchmarks

Saturation, Contamination, and Construct Validity: The Technical Limits of AI Benchmarks ELI5

How a single AI benchmark percentage hides the metric, the pass@k sampling regime, and data contamination
MONA explainer 10 min

Prerequisites for Reading AI Benchmark Scores: Metrics, Pass@k, and Contamination

Prerequisites for Reading AI Benchmark Scores: Metrics, Pass@k, and Contamination ELI5

How synthetic data generation samples new artificial records from a learned statistical distribution of real data
MONA explainer 9 min

What Is Synthetic Data Generation and How Artificial Training Data Is Created

ELI5

Four families of synthetic data generation arranged by how much statistical structure each learns from real data
MONA explainer 10 min

Rule-Based, Statistical, GAN, and LLM-Distilled: The Four Families of Synthetic Data Techniques

Rule-Based, Statistical, GAN, and LLM-Distilled: The Four Families of Synthetic Data Techniques ELI5 …

Synthetic data failure modes: vanishing distribution tails, the fidelity-privacy tradeoff, and outlier re-identification risk
MONA explainer 11 min

Model Collapse, Fidelity Gaps, and Re-Identification: The Technical Limits of Synthetic Data

Model Collapse, Fidelity Gaps, and Re-Identification: The Technical Limits of Synthetic Data ELI5

Near-duplicate training documents collapsed via MinHash signatures and LSH banding for language model data curation
MONA explainer 11 min

What Is Data Deduplication and How MinHash LSH Detects Near-Duplicate Training Samples

What Is Data Deduplication and How MinHash LSH Detects Near-Duplicate Training Samples ELI5

Geometric scatter of unlabeled points with a few highlighted near a decision boundary
MONA explainer 11 min

What Is Active Learning and How Models Pick the Most Informative Samples to Label

What Is Active Learning and How Models Pick the Most Informative Samples to Label ELI5

Diagram of uncertainty sampling selecting the most confusing data points near a classifier decision boundary
MONA explainer 11 min

Uncertainty Sampling Explained: Entropy, Margin, and Least-Confidence Query Strategies

Uncertainty Sampling Explained: Entropy, Margin, and Least-Confidence Query Strategies ELI5

Two near-identical documents flagged as duplicates while a rare unique example is silently discarded from a training set
MONA explainer 10 min

False Positives, Lost Diversity, and the Technical Limits of Deduplicating Training Data

False Positives, Lost Diversity, and the Technical Limits of Deduplicating Training Data ELI5

Three-tier data deduplication pipeline: exact hashing, fuzzy MinHash fingerprint matching, and semantic embedding clustering
MONA explainer 11 min

Exact, Fuzzy, and Semantic Deduplication: The Components and Prerequisites of a Dedup Pipeline

Exact, Fuzzy, and Semantic Deduplication: The Components and Prerequisites of a Dedup Pipeline ELI5

Diagram of an active learning loop selecting the most informative unlabeled points for human annotation
MONA explainer 12 min

Before Active Learning: Prerequisites, Building Blocks, and the Hard Limits of Query Strategies

Before Active Learning: Prerequisites, Building Blocks, and the Hard Limits of Query Strategies ELI5 …

Raw spreadsheet rows transforming into clean, scaled, and encoded numeric feature columns prepared for model training
MONA explainer 10 min

What Is Data Preprocessing and How Cleaning, Scaling, and Encoding Turn Raw Data into Training Sets

What Is Data Preprocessing and How Cleaning, Scaling, and Encoding Turn Raw Data into Training Sets …

Diagram of how data leakage inflates validation accuracy when preprocessing runs before the train-test split
MONA explainer 10 min

Data Leakage, Lost Information, and the Technical Limits of Preprocessing Pipelines

Data Leakage, Lost Information, and the Technical Limits of Preprocessing Pipelines ELI5

Diagram showing why splitting data before preprocessing keeps test-set statistics out of the model's learned transforms.
MONA explainer 10 min

Before You Preprocess: Data Types, Distributions, and Train-Test Splits You Need to Understand First

Before You Preprocess: Data Types, Distributions, and Train-Test Splits You Need to Understand First …

Two overlapping data distributions drifting apart as synthetic training samples push one curve away from the real-world curve
MONA explainer 11 min

When Data Augmentation Helps and When It Hurts: Distribution Shift and Label Corruption

When Data Augmentation Helps and When It Hurts: Distribution Shift and Label Corruption ELI5

Raw images and text converting into labeled ground-truth examples that train a supervised classifier
MONA explainer 11 min

What Is Data Labeling and Annotation, and How Ground-Truth Labels Train Supervised Models

What Is Data Labeling and Annotation, and How Ground-Truth Labels Train Supervised Models ELI5

How data augmentation transforms existing samples to expand training data and reduce overfitting in machine learning
MONA explainer 9 min

What Is Data Augmentation and How Transforming Samples Expands Training Data

What Is Data Augmentation and How Transforming Samples Expands Training Data ELI5

Diagram of label noise in training data distorting supervised model accuracy and benchmark leaderboard rankings
MONA explainer 10 min

Label Noise, Annotator Bias, and the Technical Limits of Human Data Annotation

Label Noise, Annotator Bias, and the Technical Limits of Human Data Annotation ELI5

Two annotators labeling the same dataset beside a chance-corrected agreement score chart for label reliability
MONA explainer 11 min

Inter-Annotator Agreement, Annotation Guidelines, and the Building Blocks of a Labeling Project

Inter-Annotator Agreement, Annotation Guidelines, and the Building Blocks of a Labeling Project ELI5 …

Visual comparison of geometric transforms, mixup, CutMix, and back-translation as data augmentation techniques
MONA explainer 11 min

Geometric Transforms, Mixup, and Back-Translation: How Core Augmentation Methods Work

ELI5

A dataset as particles where a fraction of labels glow red, showing why curation at scale never reaches zero error
MONA explainer 9 min

Why Perfectly Clean Data Is Impossible: The Technical Limits of Data Curation at Scale

Why Perfectly Clean Data Is Impossible: The Technical Limits of Data Curation at Scale ELI5

Diagram tracing how label errors, duplicates, and provenance shape what a machine learning model can learn
MONA explainer 10 min

What Is Training Data Quality and How It Determines Model Performance

What Is Training Data Quality and How It Determines Model Performance ELI5

Machine learning maps technical debt hotspots across a codebase, flagging code smells and high-risk files for refactoring
MONA explainer 10 min

What Is AI for Technical Debt and How Machine Learning Detects Code Smells and Hotspots

What Is AI for Technical Debt and How Machine Learning Detects Code Smells and Hotspots ELI5

How AI tools estimate technical debt using proxy signals like code complexity and git change frequency
MONA explainer 10 min

What AI Technical-Debt Tools Actually Measure — and Where the Numbers Break

What AI Technical-Debt Tools Actually Measure — and Where the Numbers Break ELI5

Three training-data failures shown in feature space: mislabeled points, skewed class frequencies, and a shifted distribution.
MONA explainer 11 min

Label Noise, Class Imbalance, and Distribution Shift: What to Know Before Fixing Training Data

Label Noise, Class Imbalance, and Distribution Shift: What to Know Before Fixing Training Data ELI5

Diagram of an AI-driven CI/CD pipeline scoring commit risk and reordering tests before deployment
MONA explainer 10 min

What Is AI in CI/CD Pipelines and How Automated Code Analysis and Deployment Checks Work

What Is AI in CI/CD Pipelines and How Automated Code Analysis and Deployment Checks Work ELI5