Open utterances-bot opened 1 year ago
Thanks for the article, Eugene - a long but excellent read!
Thank you for the kind words 🙇 And wow you're fast! I hadn't even shared it widely yet.
reading it piecemeal and printed it out - amazing thanks for putting this labor of love together! 🙏
Wonderful post and love the links to the papers. Thank you!! What do you think about Gorilla LLM and Toolformer? Still early days but wonder if there's a future pattern in there
great article, thanks for sharing. Lot of things to dig in
Thank you for creating such a comprehensive overview of LLM patterns.
Great Read, thanks for sharing Eugene
Lots of wisdom packed in one place. Thank you for putting it all together.
I summarized this with Claude 2. How did it do?
Here is a bullet point summary with chapter headings for the article:
Thank you for putting all this knowledge in one place, it is really helpful. I understood literally not a word of your summary on Retrieval-Enhanced Transformers. It might be an idea to write it a little more simply.
Thanks for sharing the knowlegde in such a good way. A lot of inputs gain from your blog. Well done.
"be waifu"
Thanks for putting this together Eugene. Thorough and comprehensible explanation of the SOTA. The domain and applications are expansive, so this clear and practical summary will be a useful reference for many including me!
Thank you so much for this article! This was a great read :)
thanks for the great information. Do you have any samples or a blog post where we can find more information about how to automate evals and Guardrails? thanks again.
Here's some thinking on guardrails, especially how to bootstrap them with out-of-domain data.
Thank you for the article had an amazing time reading it and got so much clarity on the concepts.
Patterns for Building LLM-based Systems & Products
Evals, RAG, fine-tuning, caching, guardrails, defensive UX, and collecting user feedback.
https://eugeneyan.com/writing/llm-patterns/