Python APIs for Large Language Models

In this hands-on workshop, you will learn how to use Python to call LLM APIs and get structured responses for research applications. By the end, you'll be able to integrate LLMs into your social science research workflow to classify data, extract insights, and analyze content at scale.

What You'll Learn:

• API Setup & Authentication: Configure access to an LLM provider using OpenRouter for research projects.
• API Call Formatting: Understand the differences between chat and completion endpoints, construct proper request formats, handle parameters like temperature and max tokens, and manage conversation context.
• Structured Output: Design prompts that return consistent JSON responses for tasks like sentiment classification, theme extraction, content coding, and demographic categorization.
• Integration & Error Handling: Parse API responses into Python objects, implement retry logic for failed requests, manage rate limits and costs when processing research datasets.

You'll Walk Away With: Best practices for LLM integration in research projects, example scripts for common research tasks, and the confidence to begin incorporating AI into your own research workflow.