How do you integrate AI into applications using APIs?

Question

Accepted Answer

Developers commonly add AI capabilities to applications by calling **AI APIs** (like OpenAI, Anthropic, or cloud AI services) — sending requests and receiving AI-generated responses, without needing to train models. Understanding how to use AI APIs is increasingly valuable.

## Using LLM APIs

```js
// call an LLM API (e.g. OpenAI/Anthropic) to add AI to your app
const response = await fetch('https://api.provider.com/...', {
  method: 'POST',
  headers: { 'Authorization': `Bearer ${apiKey}`, 'Content-Type': 'application/json' },
  body: JSON.stringify({
    model: 'a-model',
    messages: [{ role: 'user', content: 'Summarize this: ...' }],   // the prompt
  }),
});
// → get an AI-generated response (inference on a pre-trained model)
```

## Key considerations

```text
✓ PROMPTING → craft good prompts (the input shapes the output) — prompt engineering
✓ COST → API calls cost money (per token) → monitor/optimize usage; cache where possible
✓ LATENCY → AI calls are slower than typical APIs → handle async; consider streaming responses
✓ ERRORS / reliability → handle failures, rate limits, timeouts; retries
✓ NON-DETERMINISM → the same prompt may give different outputs → design for variability
✓ SECURITY → protect API keys (never in client code); validate/sanitize AI output
```

## Building AI features

```text
✓ Common features: chatbots, summarization, content generation, classification, extraction,
  semantic search (with embeddings), Q&A (with RAG)
✓ Use STRUCTURED output (ask for JSON) when integrating AI into app logic
✓ Validate AI output (it can be wrong) → don't blindly trust; have fallbacks
→ developers add powerful AI features via APIs (no ML training needed)
```

## Why it matters

Understanding how to integrate AI via APIs is valuable, increasingly-essential knowledge because it's **how most developers add AI capabilities to applications**, so it's a practical, in-demand skill.

Using AI APIs (OpenAI, Anthropic, cloud AI services) lets developers add powerful AI features without needing to train models — making it the primary way AI is integrated into applications.

Understanding **using LLM APIs** — sending requests with prompts and receiving AI-generated responses (doing inference on pre-trained models) — is the basic mechanism developers use.

Understanding the **key considerations** is particularly important for building robust AI features: **prompting** (crafting good prompts since the input shapes the output), **cost** (API calls costing money per token, requiring monitoring and optimization), **latency** (AI calls being slower, requiring async handling and streaming), **error handling** (failures, rate limits, timeouts), **non-determinism** (the same prompt giving different outputs, requiring designing for variability), and **security** (protecting API keys, never in client code, and validating AI output).

These considerations distinguish robust AI integration from naive usage.

Understanding **building AI features** — common features (chatbots, summarization, content generation, classification, semantic search with embeddings, Q&A with RAG), using structured output (JSON) for integration into app logic, and **validating AI output** (since it can be wrong, not blindly trusting it, having fallbacks) — reflects practical AI feature development.

As AI features become expected in applications, the ability to integrate AI via APIs (handling prompting, cost, latency, errors, non-determinism, and output validation) is an increasingly valuable, in-demand developer skill.

Since integrating AI via APIs is how most developers add AI capabilities (without training models) and understanding it — including the practical considerations (prompting, cost, latency, non-determinism, security, output validation) — is an increasingly essential, in-demand skill, understanding how to integrate AI into applications using APIs is valuable, increasingly-important knowledge — the primary way developers add AI features (via APIs, doing inference on pre-trained models), with the practical considerations (cost, latency, non-determinism, validation) being key to robust AI integration, and an increasingly in-demand skill as AI features become expected in applications.