> using RAG not for its ability to expose more info to the model than can fit in context, but rather for its ability to hide info from the model until its "discovered" in some way
Would you be willing to expand on this?
Would you be willing to expand on this?
Instead we put all the facts in a RAG database. Now when we ask the LLM to generate options it does so not knowing the actual answer, so they can't really be leading questions. We then take the user input, use RAG to get relevant facts, and then "reveal" those facts to the LLM in subsequent prompts.
Honestly we still didn't nail gameplay or anything, it was pretty janky but it was 2 days, a bunch of learning, and probably only 300 lines of Python in the end, so I don't want to overstate what we did. However this one detail was one that stuck with me.