How to Code AI: A Practical Starting Point

"How to code AI" usually means one of two things: building AI applications (using AI APIs) or building AI models (machine learning). Most builders need the first one.

The Two Paths

Path 1: AI Application Development (Most People)

Use existing AI models through APIs to build intelligent features. This is what I do daily.

You need: Programming skills (JavaScript, Python, or any language) + understanding of APIs + prompt engineering

You don't need: Machine learning expertise, GPU infrastructure, or a PhD

Path 2: AI Model Development (Specialists)

Train or fine-tune AI models from data. This is ML engineering.

You need: Python + math (linear algebra, statistics) + ML frameworks (PyTorch, TensorFlow) + significant compute resources

When this path makes sense: You have unique data that general models don't handle well, or you need specialized behavior that prompting can't achieve.

Building AI Applications

Step 1: Choose an API

Pick one AI provider to start. See Best AI APIs for the full comparison.

My recommendation: Start with the Anthropic Claude API. Best instruction following, excellent code generation, clean documentation.

Step 2: Make Your First API Call

Every AI API works the same way: send a message, get a response.

The core concepts:

• System prompt - Instructions that define how the AI behaves
• User message - The input from your user
• Assistant response - The AI's output
• Temperature - How creative vs deterministic the output is (0 = deterministic, 1 = creative)

Step 3: Build a Feature

Start with a simple AI feature:

1. Summarization - Give it text, get a summary back
2. Classification - Give it input, get a category back
3. Generation - Give it a prompt, get content back
4. Extraction - Give it unstructured data, get structured data back

These four patterns cover 80% of AI features in production applications.

Step 4: Handle the Real-World Stuff

Production AI features need:

• Streaming - Show responses as they generate (users hate waiting)
• Error handling - APIs fail. Rate limits, timeouts, content filters
• Caching - Don't call the API for identical requests
• Cost management - Track usage, set limits, use cheaper models for simple tasks

Using AI to Code AI

Here's the meta move: use Claude Code to build your AI features. Describe what you want:

"Add a chat interface to the dashboard that uses the Claude API. Stream responses. Include message history. Add a system prompt that makes it a helpful customer support agent for our product."

Claude Code builds the entire feature - API integration, streaming, UI, error handling - in minutes.

Going Deeper

Once you've built basic AI features:

• AI Agent Design Patterns - Build autonomous agents
• How to Code AI Agents - Technical guide to agent development
• Agentic AI Frameworks - Frameworks for complex agent systems
• Understanding AI - The complete knowledge foundation

Frequently Asked Questions

Do I need to know Python to code AI?

Should I learn machine learning to build AI apps?

How long does it take to learn AI development?