I agree. I wrote a book on LangChain and LlamaIndex [1] and was initially very enthusiastic about possible applications. However, most of what I now do is just writing simple scripts to interact with my data. I feel like my “lines of code per month” metrics are at an all time low. I wanted a local chat interface that worked with all the books I have written, and various local PDFs that I have collected on the semantic web and other technologies. I ended up with two Python scripts that use a new library embedchain (which uses LangChain) that total 30 lines of code. So easy to do this stuff, that I am not so sure about the idea of using products from the new flood of LLM startups. All of this does require either using the OpenAI APIs, or the Hugging Face APIs, or renting something like a Lambda Labs GPU server and running a 33B model (if you use FastChat, you get an OpenAI compatible API).
I would urge companies and people to build their own stuff because there is so much value in learning the tech. For off the shelf LLM tools, it is hard to beat OpenAI’s web app, Microsoft Bing+ChatGPT and Office 365, and Google’s beta Bard integrations with Google Docs, etc.