You are viewing a free preview of this lesson.
Subscribe to unlock all 10 lessons in this course and every other course on LearningBro.
Modern AI systems are primarily accessed through APIs (Application Programming Interfaces). Understanding how to integrate AI into applications, manage tokens and costs, and use vector databases for enhanced retrieval is essential for building AI-powered products.
AI API Request Flow:
+--------------+ HTTPS POST +------------------+
| Your |----------------->| AI Provider |
| Application | | (e.g. Anthropic) |
| |<-----------------| |
| | JSON Response | +------------+ |
+--------------+ | | LLM Model | |
| +------------+ |
+------------------+
A typical call involves: authentication (API key in headers), a request body (model, messages, parameters), and a JSON response (generated text plus metadata).
import anthropic
client = anthropic.Anthropic(api_key="your-api-key-here")
message = client.messages.create(
model="claude-sonnet-4-20250514",
max_tokens=1024,
system="You are a helpful assistant that explains concepts clearly.",
messages=[
{"role": "user", "content": "What is photosynthesis?"}
]
)
print(message.content[0].text)
Subscribe to continue reading
Get full access to this lesson and all 10 lessons in this course.