Yup, and the agent will happily ignore any and all markdown files, and will say "oops, it was in the memory, will not do it again", and will do it again.
Humans actually learn. And if they don't, they are fired.
Humans actually learn. And if they don't, they are fired.
The tooling that invokes the model should really define some kind of guardrails. I feel like there's an analogy to be had here with the difference between an untyped program and a typed program. The typed program has external guardrails that get checked by an external system (the compiler's type checker).
So for instance you could start by trying to classify the prompt in some way. If you use an LLM for this, you might need to get it to return a machine parsable data format. Then your harness can pattern match on the classification and use it to enrich the prompt with additional context. The challenge would be in determining how exactly you want to go about this, balancing tradeoffs such as accuracy, cost, time, etc..
For the classification step you might begin with something like "Determine whether the following prompt is a QUESTION or a STATEMENT. Respond using only one of the two words. Prompt: $PROMPT"
You could have multiple back-and-forths like this and at each round you gain more information about the prompt, and you can use that information to determine further classifications and/or context to include.
Magic. You're talking about magic. You keep re-iterating the same faith that "There's some magic way to make probabilistic text generator running in the cloud to never miss local files", where "files" is "files, knowledge graphs, databases etc.".
It doesn't matter how data is stored. You can't know when to include something relevant in the context because the whole thing including context is running in the cloud. You are not in the driver's seat. Literally anything you include locally in the prompt can and will be ignored.
As to your other point about anything you include in the prompt can and will be ignored. Yes, I agree. You could draw an analogy to how a teacher assigns an in-class reading assignment and follows it up with a reading comprehension quiz. If your mind wanders during the reading you may come to find that you will fail the quiz because “anything you include in the prompt can and will be ignored”. Therefore, the quiz result serves the purpose of an evaluation.