THE AI CHRONICLE FIELD GUIDE
AI models
How large language models, reasoning models and benchmarks actually work, in plain English. Start here to understand what a model can do, where it fails, and how to judge the next launch claim for yourself.
Start here
THE ESSENTIAL READ
What is a large language model?
How the system behind AI assistants learns and writes its answers.
Read the storyExplore AI models
13 stories
What is a large language model?
How the system behind AI assistants learns and writes its answers.
Open vs. closed AI models: what the difference means
What “open” really means, and how to choose between the two.
What AI benchmarks actually measure
What benchmark scores capture, and how to test models on your work.
Claude review: research and writing workflows
Claude for research and writing: plans, citations, limits and data.
Claude vs. ChatGPT vs. Gemini: choosing an assistant
Three assistants compared on price, context, tools and data terms.
ChatGPT review: everyday work and limitations
ChatGPT's plans, features and documented shortcomings for everyday work.
How reasoning models work, and what changes
Why some models think before answering, and what it costs you.
Context windows explained: what fits and what gets lost
What fills a model's working space, and what gets overlooked.
What is RAG? Retrieval-augmented generation explained
How AI looks things up before answering, and where it breaks.
AI hallucinations explained: why plausible answers fail
Why models make confident errors, and the checks that catch them.
Claude vs. ChatGPT for long-document work
Which assistant handles long reports and contracts, and how.
ChatGPT vs. Gemini for spreadsheet analysis
Which assistant shows its working on spreadsheet analysis.
How to judge a new AI model release
A checklist for reading launch posts, scores and system cards.