What Are AI Agents? A Clear Definition and How They Differ From Chatbots
A plain-language definition of AI agents: the loop that powers them, the parts they are built from, and how they differ from chatbots that only answer questions.
8 min read
The archive
All articles
Guides and explainers on artificial intelligence, newest first. Pick a topic to narrow things down.
Designing Human-in-the-Loop AI Workflows
A practical framework for placing people inside AI workflows: where to require approval, how to escalate, and why reversibility should drive oversight decisions.
8 min read
How to Evaluate AI Agents Before You Trust Them
A step-by-step approach to evaluating AI agents: building task suites, defining success criteria, reviewing trajectories, and catching regressions before users do.
8 min read
Multi-Agent Systems: When One Agent Isn't Enough
How multi-agent AI systems split work between orchestrators and workers, how handoffs pass context, what they cost, and when a single agent is the better choice.
7 min read
Embeddings: How Machines Represent Meaning
What embeddings are, how vector spaces capture similarity, and how teams use them for search, recommendations, clustering, and deduplication.
8 min read
Why AI Models Hallucinate, and What You Can Do About It
Language models sometimes state false things with total confidence. Here is why that happens mechanically, and the practical controls teams use to catch and reduce it.
8 min read
Tool Use and Function Calling: How Language Models Take Actions
How language models call tools: writing schemas, running the request-execute-return loop, handling errors, and scoping permissions so models act safely.
8 min read
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
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