TL;DR

AI APIs let you add AI capabilities to apps without training models. Authenticate with API keys, send HTTP requests with prompts, handle responses and errors, and optimize for cost and performance.

What are AI APIs?

Definition:
Web services that provide AI capabilities via HTTP requests.

Common AI APIs:

  • OpenAI (GPT-4, DALL-E)
  • Anthropic (Claude)
  • Google (Gemini, Vertex AI)
  • Cohere (embeddings, generation)

Basic API workflow

  1. Get API key: Sign up, retrieve credentials
  2. Send request: HTTP POST with prompt/input
  3. Receive response: JSON with AI output
  4. Handle errors: Retry logic, fallbacks
  5. Process result: Use in your app

Making a request (example)

import openai

openai.api_key = "your-key-here"

response = openai.chat.completions.create(
  model="gpt-4",
  messages=[
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Explain APIs simply."}
  ]
)

print(response.choices[0].message.content)

Authentication

API keys:

  • Include in request headers
  • Keep secret (never commit to Git)
  • Rotate regularly

OAuth (some services):

  • For user-specific access
  • More secure for multi-user apps

Request parameters

Required:

Optional:

Response handling

Parse JSON:

  • Extract generated text
  • Get token usage
  • Check finish reason

Stream responses:

  • For real-time output
  • Display as generated
  • Better UX for long outputs

Error handling

Common errors:

  • 401: Invalid API key
  • 429: Rate limit exceeded
  • 500: Server error

Retry logic:

  • Exponential backoff
  • Max retry attempts
  • Different error handling per code

Rate limits

Types:

  • Requests per minute (RPM)
  • Tokens per minute (TPM)
  • Concurrent requests

Strategies:

  • Queue requests
  • Batch when possible
  • Upgrade tier if needed

Cost optimization

  • Cache common responses
  • Use smaller models when possible
  • Limit max tokens
  • Batch requests
  • Monitor usage dashboards

Security best practices

  • Never expose API keys client-side
  • Use environment variables
  • Implement server-side proxy
  • Validate/sanitize user input
  • Set spending limits

Testing

  • Start with playground/console
  • Unit test with mocked responses
  • Integration tests in staging
  • Monitor production carefully

What's next

  • Building AI Applications
  • Prompt Engineering
  • Cost Optimization Strategies