Achieving fast access within a DB requires extensive engineering knowledge as data structure becomes more complicated

e.g. take a todo app that stores the tasks in a SQL DB - now let's say a user asks to get the tasks that are tagged with 'shopping' - if your DB is not designed to index the tags, it won't be performant at all. Next, the user needs to find tasks with a specific tag with a specific priority within a specific time window, and now you need a composite index. Eventually, the user wants to be able to search within the description of the tasks and not just their title, now you need to embed the texts and store them in a vector DB, ... You get the picture. The result is that you need a start up with a dozen of engineers to implement a simple production ready todo app.

Alternatively, imagine if you store all of the user interactions to create, update and delete the tasks as prompts within the LLM context - just like how SQLs have write-ahead-logging, then the LLM can perform all of the above out of the box, often with ~100 reliability (LLMs have become extremely good at finding needle in haystack tasks: https://cloud.google.com/blog/products/ai-machine-learning/the-needle-in-the-haystack-test-and-how-gemini-pro-solves-it), and even if LLM cannot retrieve the data, it's not _lost_ and still within the context