I couldn’t have asked for a denser review of such a technical and diverse domain. It’d been more a year since I graduated (majored in computer science and minored in artificial intelligence) and so I decided to brush up on some basics and consolidate what I’d studying since the past ~4 years.
I also picked up the book to systematically populate my org-roam buffer and have my root node rooted here in the braindump.
I was initially reluctant on using generative AI for my writing process.
That being said, I was quite aware of the potential of large language models (generically addressed as LLMs in here henceforth) - especially true in the case of content creators and/or eccentrically curious individuals.
I, therefore, decided to clarify how I’ll be using generative AI for my ideation process.
Before we get onto that, as promised by the title, distilling the over-arching skills needed to extract good insights from a conversation with an LLM (an el-el-em; please don’t read it as large, please..).
So, I’ve been reading a book on prompt engineering and decided to practice a little…
Here’s a conversation I had with chatGPT regarding a small essay I wrote recently and fortunately, there’s room to improve.
Here’s how it went…
Raj: You are my writing critic for this session; do not make any intellectual changes in any text I paste; do not heavily influence my style; only analyze what I say and generate pointers as to how I can become a better writer.
I took this up when I had to setup a bunch of NLP pipelines for work and this does stand true to its name - it is a quick and practical index into approaches, introductory theory and useful libraries for the same.
I don’t like reading text books at a stretch due to several reasons:- “cross a bridge, when you get to it” is something that has stood the test of time for me when it comes to reading practical books. I don’t find it useful to read stuff without the doing if that is what the book is intended for.
