> In essence, the "Demonstrate" stage is about automatically generating comprehensive, pipeline-aware examples that show not just what you want to achieve, but also how to achieve it through a series of intermediate steps. This approach enables the framework to handle complex, knowledge-intensive NLP tasks more effectively than simpler "retrieve-then-read" pipelines.
This is an excellent and unique guide, thanks man! ✌🏼
Thank you!
Great article. I haven't encountered this approach elsewhere. I'll get my hands dirty and test it on my prompts now ;)
Good to see how rapidly your knowledge about LLM's evolved during last couple of months. Keep up the good work!
Fantastic and full of great insights article! Loved every bit of it! Thanks!
Structuring the prompt with html tags is a great idea. Thanks
Fantastic, thanks for sharing.
Examples recommendations align well with the "Demonstrate" stage in the Stanford's DSP (Demonstrate-Search-Predict) framework.
Thank you. Didn't know that one; I'll check it out.
> In essence, the "Demonstrate" stage is about automatically generating comprehensive, pipeline-aware examples that show not just what you want to achieve, but also how to achieve it through a series of intermediate steps. This approach enables the framework to handle complex, knowledge-intensive NLP tasks more effectively than simpler "retrieve-then-read" pipelines.