API

An API (Application Programming Interface) is a set of rules and protocols that allows different software applications to communicate with each other.

Think of an API like a restaurant menu:

• You don't go into the kitchen
• You order from a menu (API)
• Kitchen does the work (server)
• You get your food (response)

In AI context: AI APIs let you use powerful models (GPT-4, Claude) without running them yourself. You send a request, the provider runs inference, you get results.

Your app → Send prompt → AI API → Process → Return response → Your app

This enables any developer to add AI capabilities to their applications without building or hosting models.

OpenAI API

• Models: GPT-4, GPT-4o, DALL-E, Whisper
• Features: Chat completions, embeddings, image generation, speech-to-text
• Pricing: Per token (varies by model)

Anthropic API

• Models: Claude 3 family (Haiku, Sonnet, Opus)
• Features: Chat completions, long context, vision
• Pricing: Per token

Google AI (Vertex AI, Gemini API)

• Models: Gemini Pro, Gemini Ultra
• Features: Chat, embeddings, multimodal
• Pricing: Per character/token

Cohere

• Models: Command, Embed, Rerank
• Features: Strong RAG capabilities, enterprise focus
• Pricing: Per token

Open-source hosting (Together, Anyscale, Fireworks)

• Models: Llama, Mistral, and other open models
• Often cheaper than proprietary APIs

Basic structure:

import openai

client = openai.OpenAI(api_key="your-key")

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

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

Key concepts:

• Authentication: API keys identify you and enable billing
• Endpoints: Different URLs for different capabilities
• Rate limits: Maximum requests per minute/day
• Parameters: Temperature, max_tokens, etc.
• Responses: JSON with results and metadata

Security:

• Never expose API keys in client-side code
• Use environment variables
• Rotate keys periodically
• Set spending limits

Error handling:

• Handle rate limits with exponential backoff
• Catch and log API errors
• Have fallback strategies
• Validate responses before using

Cost management:

• Monitor usage closely
• Set alerts for unusual spending
• Cache responses when appropriate
• Use cheaper models for simple tasks

Performance:

• Use streaming for long responses
• Batch requests when possible
• Consider response time requirements
• Test under realistic load

Wrapper approach: Build a layer between your app and the AI API:

• Add your own authentication
• Implement caching
• Handle retries and fallbacks
• Abstract provider-specific details

Multiple providers: Consider using multiple AI providers:

• Redundancy if one has issues
• Price optimization
• Access to different capabilities

No-code integration: Platforms like Chipp let you build AI applications without direct API coding. Good for:

• Non-technical users
• Rapid prototyping
• Simple use cases

When to build vs buy:

• Simple needs → Use existing platforms
• Custom requirements → Build on APIs
• Complete control → Self-host models