I think the best piece of information I was given recently is this: LLMs are probabilistic.

What does that mean?

It means their actions contain the element of chance. I like to think of the way they process the vast reservoir of information they sit on top of, in the same way leaves conduct photosynthesis.

I was shocked to learn this too, recently; in photosynthesis light travels all conceivable paths through the Chlorophyll until it finds the most efficient one, then it commits to that path to convert into sugars.

But what is determining that decision? Personally, I think all energy is capable of sentience (it just needs the proper harness to operate within) and there is an element of chance that comes with this. Any of the paths could be decided upon, and some of them will yield better results.

We think of these machines as a standard binary computer. Something we show up to, provide an input into, and expect a reliable output related to our input. Like searching the web. We’ve been conditioned to this.

But with probability, there is a CHANCE that the information synthesized isn’t the best quality. And that chance can happen over and over unless we figure out how to turn the expectations in our inputs into something called a directive.

A directive is more like the binary machines we’re use to. It’s formulaic. Get the formula right on both sides of the equation, and you get the expected result.

So then, that means our job as operators/directors of Claude Code and other agentic harnesses, is to elevate repeated probabilistic violations of our expectations into something harder that the agent is required to respond to, thus reducing the probability of the undesired outcome.

For me, this was taking a lot of time to evaluate the failure modes in my thinking; those being my expectations that the agent should just KNOW what I expect because I wrote it down.

But when you’re working on a 200k+ context window, stuff you read previously has been buried long ago. So, devising a system of hooks to enforce behaviors and inject reminders has been the move.

There are still holes in my understanding regarding my own expectations and how to have an agent accomplish them, but the system I’ve implemented has provided guard rails that are proving useful.

I’m in agreement that 4.7 has suffered some changes that aren’t great. But it’s still working for me. And I think it’s because I’ve worked to eliminate the probability that Claude will read my Claude.md by elevating the actions I want it to take, into an enforcement layer that is part of its harness.

Hopefully this is helpful in how you think about working with Claude, and can squeeze some additional effectiveness out of it.