You still need to find the correct data, and get it to the LLM. IMO, a lot of it is data engineering work with API calls to an LLM as an extra step. I'm current

You still need to find the correct data, and get it to the LLM. IMO, a lot of it is data engineering work with API calls to an LLM as an extra step. I'm currently doing a lot of ETL work with Airflow (and whatever data {warehouses, lakes, bases} are needed) to get the right data to a prompt engineering flow. The prompt engineering flow is literally a for loop of Google Docs in a Google Drive that non-tech people, but domain experts in their field, can access. It's up to the domain experts and me to understand where giving it data will tone down the hallucinative nonsense an LLM puts out, and where we should not give data because we need the problem solving skills of the LLM itself. A similar process is for tool-use, which in our case are pre-selected Python scripts that it is allowed to run.