Brain dump but tldr: while it is objectively difficult to say what is a good essay, there are alot of interesting ways to evaluate that are imo currently unexplored - creativity issue. Alot of this can be solved with prompt engineering/scaffolding

Lets start with generating completions to be graded. I think we are all in agreement that prompting only with "Write me and essay about X" gives a bunch of garbage slop. The simplest solution is RAG. Give it a bunch of relevant "research" and get it to write. This way, you have a controllable way to inject diversity into the completions by changing the docs provided. There are of course more complicated ways (multi-model, multi-prompt, persona stuff etc). 

I think this can be applied to evals too rather than thinking about llm as a judge as simply "Score this essay from 1 to 10", there are alot more interesting ways imo. 

We wont have ground truth verifiers like math and code but i think we will have more interesting ways to evaluate in ways that llm as a judge would work quite well. For the RAG example, a grading metric could be how well does the output make use of the provided docs. For persona stuff, you can ask how likely will a given persona output a given piece of text. Evaluation could range from llm as a judge to retrieval/NLI entailment stuff. 

Some more ideas off the top of my head:
- Get a model to write a counterargument essay. Evaluate if the counterargument or OP is better. (i am aware this requires the subjective notion of "better")
- Generate an essay plan. use the essay plan to get an output. Ask a model to guess the essay plan from the original essay plan. How similar are the 2?

The point isn't that llm-as-a-judge is good or sufficient (it isnt). But rather, so many people think of it as just asking a model "How good is X? Is X or Y better?" There are alot of interesting ways to provide better feedback signals using an llm which imo is really underexplored.