BerriAI / litellm
github.com·3d ago
Developer Tools
Real Python·Jul 24·5 min read
The Real Python Podcast – Episode #304: Configuring a Versatile LLM Harness & Scraping the Web With Scrapy
TL;DR
Ayan Pahwa discusses the importance of the harness around large language models (LLMs) and shares insights on web scraping with Scrapy. He emphasizes the transition from prompt engineering to context and loop engineering for building efficient developer workflows.
✦ Why It Matters
Consider enhancing your web scraping capabilities by exploring Scrapy and its extensions today.
Key Takeaways
- Explore building extensions for Scrapy to enhance web scraping functionality.
- Implement self-hosting setups for better control over Python applications.
- Transition from prompt engineering to context and loop engineering for LLMs.
- Manage token usage effectively to optimize LLM performance.
How It Works
Harness engineering involves creating a structured environment around LLMs that facilitates efficient interaction and data processing. By focusing on context and loop engineering, developers can improve how LLMs understand and respond to prompts, leading to more effective outcomes.