Couple more that I found useful: >List all the entities in the text that are ambiguous. Entities are ambiguous if you don't understand them, if they have multi

Couple more that I found useful: >List all the entities in the text that are ambiguous. Entities are ambiguous if you don't understand them, if they have multiple meanings and you can't decide which one from the context, or are phraseal terms whose meaning can be different in different cultures. This allows the LLM to identify parts of previous prompt that the AI did not fully understand. >List all the entities and relationships to other entities. Be precise, avoid duplicates, list each relationship only once. This allows to compress the past context for longer tasks. >Make a list of Google searches needed to build a factual answer, one sentence per search. You get the list then >For each sentence extract the main entity, decide whether to find more information about it on Google, wikipedia, ..., answer in json This is my "ghetto" agent, it's not iterative, but it's stable enough to handle the unexpected > It's the context enough to answer factually the user question? Answerer yes or no. Only answer yes if you are sure you can answer To make the agent able to loop, or ask user for more context.