Most AI content falls into one of three traps:
Too academic. Attention mechanisms. Transformer architectures. Gradient descent. You don't need a PhD to add AI features to your app.
Too superficial. "Just prompt ChatGPT better!" isn't a development strategy.
Too ML-focused. Training models and curating datasets is a different job. You're building products, not publishing papers.
You need something in between: practical mental models that help you make good decisions without drowning in theory.
This 16-page guide gives you:
The three AI primitives — Every AI feature is some combination of generate, classify, and search. Once you see this, everything clicks.
How LLMs actually work — Not the math, but the mental model. Why they hallucinate, why they're stateless, and what that means for your code.
The provider landscape — OpenAI vs Anthropic vs Google vs open source. When to use what, and how to think about the tradeoffs.
Five implementation patterns — Chat, content generation, classification, RAG, and agents. Enough detail to get started, without the overwhelm.
What to learn next — Based on what you're building, here's where to go deeper (and what to skip).
This guide is for you if:
You write code (any language) and want to add AI features
You're curious about AI but haven't gone deep yet
You want practical knowledge, not academic theory
You'd rather build products than train models
Not for you if you're already building ML pipelines or fine-tuning models. You're past this.
After 20-30 minutes with this guide, you'll be able to:
✓ Choose the right AI approach for your feature
✓ Explain AI concepts to non-technical colleagues
✓ Evaluate providers based on your actual needs
✓ Know what to learn next (and what to skip)