#ai #langchain #python #reading-list

🔗 Lessons after a half-billion GPT tokens
kenkantzer.com

My startup Truss (gettruss.io) released a few LLM-heavy features in the last six months, and the narrative around LLMs that I read on Hacker News is now starting to diverge from my reality, so I thought I'd share some of the more "surprising" lessons after churning through just north of 500 million tokens, by my estimate.

Some details first:

  • we're using the OpenAI models, see the Q&A at the bottom if you want my opinion of the others
  • our usage is 85% GPT-4, and 15% GPT-3.5
  • we deal exclusively with text, so no gpt-4-vision, Sora, whisper, etc.
  • we have a B2B use case - strongly focused on summarize/analyze-extract, so YMMV
  • 500M tokens actually isn't as much as it seems - it's about 750,000 pages of text, to put it in perspective
continue reading on kenkantzer.com

⚠️ This post links to an external website. ⚠️

You might also like:

If this post was enjoyable or useful for you, please share it! If you have comments, questions, or feedback, you can email my personal email. To get new posts, subscribe use the RSS feed.