Lesson 4: AI Integration with APIs and Code

From Chat to CodeInteracting with AI through a web interface is useful, but the true power of generative models lies in [API Integration]. By using an API, you can pipe data from your database directly into an LLM and parse the output back into your application. We will use a standard request structure. Below is a conceptual example of a request: import openai; response = openai.chat.completions.create(model='gpt-4o', messages=[{'role': 'user', 'content': 'Explain API latency.'}]); print(response.choices[0].message.content). This code sends a request to the model and waits for a JSON response. You must manage your [API Keys] securely; never hardcode them into public repositories. Use environment variables to keep secrets safe. For official documentation on API calls, visit the OpenAI API Reference and Google's Gemini API documentation. Understanding [Rate Limits] is also essential, as these define how many requests you can make in a minute or day, preventing service disruption. FAQsWhat happens if my API key is leaked?If an API key is exposed, malicious actors can rack up charges on your account. Rotate it immediately in the developer console. How do I handle AI errors in code?Always use try/except blocks to catch API errors, such as rate limits or model timeouts. Is it cheaper to use an API than the ChatGPT Plus subscription?Yes, for many use cases, pay-per-token API usage is significantly more cost-effective than a flat monthly fee.