The frontier problems
LLM evaluation is a young, fast-moving field. The hardest problems are still open. Knowing them keeps you honest about the limits of what you can promise.
Eight unsolved evaluation problems
- Evaluating creativity — how do you score a poem, a novel idea, a joke? Multiple valid outputs, taste is subjective, no reference.
- Evaluating reasoning — correct answer ≠ correct reasoning process. A model can pattern-match its way to the right answer without understanding.
- Evaluating safety at scale — adversarial attacks evolve faster than defenses. A safety eval today is a stale snapshot tomorrow.
- Long-context evaluation — how do you test 1M-token inputs systematically? Most benchmarks are short. Behavior at long context is qualitatively different.
- Multi-modal evaluation — scoring outputs that mix text, images, video, audio. Each modality needs its own framework.
- Agent evaluation — open-ended tasks with no single correct trajectory. How do you score "did this 47-step session do the right thing"?
- Meta-evaluation — making sure your metrics actually predict quality. The hardest meta-question.
- Benchmark contamination — public benchmarks all leak into training data eventually. The half-life of a benchmark's usefulness is shrinking.
Principle: You will not solve all of these. But understanding that evaluation is hard — and doing it anyway — puts you ahead of 90% of teams that ship LLM features. If you cannot measure it, you cannot improve it. Start measuring.
Where the field is going
- More capability-eliciting evals — designed to surface latent abilities (and dangers) rather than measure performance.
- More process evaluations — scoring not just final outputs but the reasoning trace.
- More continuous evals — production traffic feeds back into the suite in real time.
- More secret holdouts — held-back datasets that never appear in any training corpus.
- More collaborative grading — humans + LLM judges + classifiers ensembled.
The eval mindset
The discipline is not "do I have a perfect metric?" It is "do I have an honest measurement, and am I improving it as the system improves?" Evaluation is a practice, not an artifact. Teams that internalize this ship faster, regress less, and trust their own systems.