AI for Developers
API, architecture, and integration-level content for building with AI, not just using it.
Hallucination Debugging: Concrete Techniques for Trustworthier Model Output
A model stating something false with total confidence isn't a bug you can patch out - it's a pattern you have to design around. Here are the practical techniques that actually move the needle on hallucination risk.
What RAG Really Involves (It's More Than "Connect Your Data")
Retrieval-Augmented Generation gets pitched as plug-and-play - hook up your data, get grounded answers. Here's the actual mechanics, and the specific spots where real implementations trip up.
Getting an LLM to Output JSON You Can Actually Trust
Tell a model to 'return JSON' and what you often get back is JSON-shaped, not JSON-valid. Here's a practical look at what actually gets you reliable, parseable structured output from an LLM.
Hosted AI API or Self-Hosted Model: A Framework for Deciding
Route your requests through a provider, or run the model yourself? A practical way to weigh cost, control, and operational complexity between the two paths.
Tokens, Rate Limits, and Cost: How AI API Pricing Actually Works
How AI API billing really works under the hood - tokens, throttling limits, and the details that make one integration far pricier than another - so you're not blindsided by the invoice.
Your First LLM API Call, Explained Piece by Piece
What actually happens under the hood the first time you hit an LLM API - the request body, the response you get back, and the slip-ups beginners run into again and again.