Overfitting: Why Models Memorize and How to Stop It
Why a model can look excellent in training and fail on real data, how to spot overfitting early, and the practical techniques that keep models honest.
8 min read
The archive
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Guides and explainers on artificial intelligence, newest first. Pick a topic to narrow things down.
Tokens and Context Windows: What They Mean for Your AI Projects
What tokens and context windows are, how they drive the cost and speed of language model applications, and how to budget context sensibly in real projects.
8 min read
Prompt Engineering Techniques That Hold Up in Practice
Durable prompting techniques for real applications: clear instructions, examples, structure, context, task decomposition, output formats, and testing prompts like code.
8 min read
Data Quality for Machine Learning: The Unglamorous Work That Decides Results
The data problems that quietly sink machine learning projects, from label noise and leakage to drift, and a practical checklist for catching them early.
8 min read
Fine-Tuning vs. Prompting vs. RAG: Choosing the Right Approach
A practical decision framework for choosing between prompting, retrieval-augmented generation, and fine-tuning, including when to combine them and what each really costs.
8 min read
The Transformer Architecture, Explained Without the Math Wall
A plain-language tour of the transformer: how attention works, why position has to be added in, and what each layer contributes to the final result.
8 min read
Choosing a Language Model for Your Product: A Criteria Checklist
A practical checklist for picking a language model for a real product, covering task quality, latency, cost, privacy, deployment options, and the risk of depending on a vendor.
8 min read
How to Scope Your First AI Project So It Actually Ships
A step-by-step way to pick, frame, and bound a first AI project so it reaches real users instead of stalling as a promising demo that never leaves the pilot stage.
9 min read
How to Evaluate a Machine Learning Model
A practical guide to precision, recall, F1, ROC-AUC, and calibration, and how to choose evaluation metrics that reflect what mistakes actually cost.
8 min read
Practical AI Use Cases for Small Businesses (and the Ones to Skip)
Where AI tools genuinely save time in a small business, where they quietly create risk, and a simple way to decide which tasks are worth handing over first.
8 min read
Measuring the ROI of AI Projects Without Fooling Yourself
How to estimate what an AI project actually returns: honest baselines, full costs, the price of errors, and comparisons that separate real gains from wishful accounting.
7 min read
Supervised, Unsupervised, and Self-Supervised Learning: A Practical Comparison
How the three main ways of learning from data differ, what each one needs from you, and how to pick the right approach for a real project.
9 min read