| I work in cloud consulting with a focus on app development. But I can do almost any vertical except Big Data/ML (learning that now). I use LLMs in every step of the process. Anytime we talk to the client on a call we use Gong for text transcription (https://help.gong.io/docs/see-a-call-transcript). After the call is transcribed, it summarizes the call, gives you items to follow up and has chapters as the conversation topic changes. Once I get all of the documentation, statements of work, any artifacts that we come up with, etc, I use Google’s NotebookLM. I put all of the artifacts in it including transcripts before I came in the project. You can then ask it questions and it will give you answers either with citations to your sources that you included. I use ChatGPT along with NotebookLM to write the assessments and requirement docs along with a project plan. I’ve been doing this type of writing for awhile before LLMs were a thing so I do a lot of prompt iteration and editing so it sounds like me and not “AI Slop” (https://news.ycombinator.com/item?id=42909042). After the project is signed, I then become a tech lead. ChatGPT has been well trained on the AWS SDK for various languages, Terraform , the CDK etc. I use it to write scripts and Lambdas involving AWS. I don’t get a chance to do as much hands on coding as I use to between working with sales and as a tech lead. I once used it to create a simple Hello World API. I was demonstrating to a Java shop how to deploy APIs to Lambda, ECS (AWS’s Docker orchestration service) and EC2 via Ci/CD. I was upfront with then about not knowing Java. I am a c#/Node/Python guy. All of the tools I mentioned are specifically approved by my company and we use GSuite as our corporate standard and I think we added the pro version of NotebookLM to each seat a couple of weeks ago. |