Tutorials
How-to and getting-started guides
Ship an AI feature responsibly: a checklist
A practical pre-launch checklist for AI features — covering accuracy, safety, privacy, transparency, and the human safeguards that keep users protected.
Test your prompts like code
A prompt is code that ships to users. Treat it that way — with test cases, a baseline, and a regression check before every change.
Prompt engineering fundamentals that still matter
Trends in prompting come and go. A small set of fundamentals keeps working across models and releases. Here they are, with the reasoning behind each.
Add citations to AI answers
Citations turn an unverifiable answer into a checkable one. Here is how to get a model to cite its sources, and to cite them honestly.
Choose the right model size for a task
Bigger is not always better. A practical method for picking a model size that matches the task, the budget, and the latency you can live with.
Set up a feedback loop to improve answers
An AI feature that never learns from its mistakes stays stuck. How to capture signal, turn it into examples, and close the loop that makes answers better.
Reduce hallucinations: a practical checklist
Models invent facts when the task invites them to. This checklist covers the moves that cut hallucinations without pretending you can eliminate them.
Measuring quality: how to set up a basic eval
Vibes don't scale. A small, honest evaluation turns 'this feels better' into a number you can trust — here's how to build one from scratch.
Chunk documents well for retrieval
Retrieval is only as good as its chunks. Here is how to split documents so the right passage comes back whole and in context.
Stream and render model output in a UI
Why streaming makes AI features feel fast, and how to render token-by-token output in a UI without flicker, broken markup, or layout chaos.
Build a simple RAG pipeline: a conceptual walkthrough
Retrieval-augmented generation, built up one stage at a time. No magic, no specific stack — just the shape of the pipeline and the decisions that matter.
Cost control 101: keeping an AI feature affordable
AI features bill by the token, and small habits compound into large invoices. Here are the durable levers for keeping cost in line without gutting quality.
Handle errors and timeouts gracefully
Model calls fail, stall, and rate-limit. A practical guide to retries, timeouts, fallbacks, and fail-safe behavior that keeps an AI feature reliable.
Write a system prompt that works
A system prompt sets the rules before the conversation starts. Here is how to write one that holds up across real inputs, not just demos.
Your first AI agent: a minimal, honest build
An agent is a model in a loop with tools. Build the smallest honest version, understand why it works, and learn where it goes wrong before adding ambition.
Few-shot prompting: a practical guide
Examples teach a model faster than instructions. Here is how to choose, order, and format them so few-shot prompting reliably pays off.