Building applications on top of large language model APIs is one of the most in-demand developer skills of 2025. This guide walks you through everything from your first API call to building production-ready AI features.
Choosing Your LLM API
The three most developer-friendly APIs are:
- OpenAI (GPT-4o, GPT-4.1) — Most widely supported, largest ecosystem
- Anthropic (Claude 3.5 / Claude 4) — Best for long documents, nuanced reasoning, safety-critical use cases
- Google (Gemini 2.0) — Excellent multimodal support, tight Google ecosystem integration
For most applications, start with the OpenAI API. Its SDKs are the most mature and the pattern you learn transfers directly to other providers.
Authentication and Setup
Install the SDK: npm install openai or pip install openai. Store your API key in an environment variable — never hardcode it:
// Node.js
import OpenAI from "openai";
const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });Making Your First API Call
const response = await client.chat.completions.create({
model: "gpt-4o",
messages: [
{ role: "system", content: "You are a helpful assistant." },
{ role: "user", content: "Explain RAG in one paragraph." }
],
max_tokens: 200
});
console.log(response.choices[0].message.content);The three key parameters are model, messages, and max_tokens. Start here, then add temperature, streaming, and tool use as needed.
Prompt Engineering for Developers
Three patterns that consistently improve output quality:
System prompts — Define the AI’s role and constraints in the system message. Be specific: “You are a JSON API. Always respond with valid JSON. Never include explanation text.”
Few-shot examples — Show the model what good output looks like before asking for new output. This dramatically improves consistency.
Output structure — Tell the model exactly what format you want: “Respond in this JSON schema: {sentiment: string, confidence: float, reasoning: string}”
Handling Streaming Responses
For user-facing applications, stream tokens as they arrive rather than waiting for the full response:
const stream = await client.chat.completions.create({
model: "gpt-4o",
messages: [{ role: "user", content: userMessage }],
stream: true
});
for await (const chunk of stream) {
process.stdout.write(chunk.choices[0]?.delta?.content || "");
}Managing Costs
Cost = (input tokens + output tokens) × price per 1M tokens. To keep costs low: cache frequent prompts, use smaller models for simple tasks (gpt-4o-mini costs 15× less than gpt-4o), and set max_tokens aggressively.
Add a cost estimator to your dev environment: count tokens with tiktoken before sending expensive requests.
Error Handling
Always handle rate limit errors (429), timeout errors, and context length errors. Implement exponential backoff for retries. Log all API calls for debugging and cost tracking.
