If your forecast is just one number, it’s probably telling an incomplete story. Here’s how prediction intervals add the context city leaders actually need to plan ahead.

A vague prompt gets unpredictable results. A specific one gets reliable results. Here's how to write prompts that actually work, using techniques you already know from managing people.

LLM hallucination is manageable, but not by waiting for smarter models. The real solution is giving the model less room to guess. Here are four techniques that make AI outputs dramatically more reliable.

LLMs can only hold so much information at once. Understanding context windows helps explain why long conversations sometimes go off the rails.

LLMs can generate confident, detailed responses that are completely wrong. Understanding why this happens helps you use these tools effectively.

When ChatGPT responds to your prompt, it's not retrieving an answer from a database. It's predicting the next word, one token at a time, based on probability.

We rebuilt our popular data visualization guide with interactive examples that let you see the principles in action.

Learn how adding semantic search to your city's toolbox can dramatically improve search accuracy and make your website easier for everyone to use.

How can a computer tell that "blueberry" is more like "strawberry" than "red"? It starts by turning language into math, unlocking smarter tools for cities in the process.

Get up to speed on the fundamentals of artificial intelligence, from machine learning and generative AI to neural networks and prompt engineering, all tailored for how cities can put these tools to work. This first installment of AI for Cities breaks down complex tech into clear, practical language for local government leaders ready to explore the future of smarter, more efficient city operations.