|
|
|
|
|
|
by pgbovine
1118 days ago
|
|
|
Cool work! You and your team may be interested in these two recent CHI papers from Microsoft Research, both on very relevant topics to what you've been doing: 1) “What It Wants Me To Say”: Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models (https://arxiv.org/abs/2304.06597) -- they try to tackle a similar problem as what you described above 2) On the Design of AI-powered Code Assistants for Notebooks (https://arxiv.org/abs/2301.11178) - uses Mito as part of their case study |
|
|
> Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models
I love the idea of giving users feedback on how to get better at prompting the LLM. I think the key to using this approach within Mito is giving users guidance at the right time -- sometimes shorter prompts get the job done, and they're always easier to write :)
A really sweet integration of this approach could be: when the LLM generated code errors or when we notice that the user undoes their previous prompt, we offer the user help in converting non-working prompts into ones that follow best practices of breaking complex tasks down into small steps.
> On the Design of AI-powered Code Assistants for Notebooks - uses Mito as part of their case study
Andrew McNutt, one of the authors presented this paper here: https://www.youtube.com/watch?v=g0prh8mE3bI Their different classifications of notebook code-gen tools has actually been super helpful in my own thinking. Thanks for the help, Andrew if you're a HNer