LLM APIs for Developers

Overview

LLM APIs let you integrate AI capabilities directly into your applications. Whether you are building a coding assistant, chatbot, content generator, or data analysis tool, understanding how to call LLM APIs effectively is an essential developer skill.

API Comparison

Example: Claude API (TypeScript)

import Anthropic from '@anthropic-ai/sdk';

const client = new Anthropic();

const message = await client.messages.create({
  model: 'claude-sonnet-4-20250514',
  max_tokens: 1024,
  messages: [
    {
      role: 'user',
      content: 'Write a TypeScript function to validate email addresses.',
    },
  ],
});

console.log(message.content[0].text);

Best Practices

• •Always set max_tokens to prevent runaway costs
• •Implement retry logic with exponential backoff for rate limits
• •Cache LLM responses when the same prompt produces the same result
• •Use streaming for long responses to improve perceived latency
• •Store API keys in environment variables, never in code
• •Monitor usage and set billing alerts to prevent surprise costs