conclusion

app/src/content/article.mdx

@@ -111,7 +111,20 @@ This is precisely when you could want to create your own evaluation.
 
 ## Conclusion
 
+Evaluation is both an art and a science. We've explored the landscape of LLM evaluation in 2025—from understanding why we evaluate models and the fundamental mechanics of tokenization and inference, to navigating the ever-evolving ecosystem of benchmarks, and finally to creating evaluations for your own use-cases.
 
+Key things I hope you'll remember are:
 
+**Think critically about what you're measuring.** Evaluations are proxies for capabilities, so a high score on a benchmark doesn't guarantee real-world performance. Different evaluation approaches (automatic metrics, human judges, or model judges) each come with their own biases, limitations, and tradeoffs. 
+
+**Match your evaluation to your goal.** Are you running ablations during training? Use fast, reliable benchmarks with strong signal even on small models. Comparing final models for selection? Focus on harder, uncontaminated datasets that test holistic capabilities. Building for a specific use case? Create custom evaluations that reflect your problems and data.
+
+**Reproducibility requires attention to detail.** Small differences in prompts, tokenization, normalization, templates, or random seeds can swing scores by several points. When reporting results, be transparent about your methodology. When trying to reproduce results, expect that exact replication will be extremely challenging even if you attempt to control for every variable.
+
+**Prefer interpretable evaluation methods.** When possible, functional testing and rule-based verifiers should be chosen over model judges. Evaluations that can be understood and debugged will provide clearer and more actionable insights... and the more interpretable your evaluation, the more you can improve your models!
+
+**Evaluation is never finished.** As models improve, benchmarks saturate. As training data grows, contamination becomes more likely. As use cases evolve, new capabilities need measuring. Evaluation is an ongoing battle!
+
+To conclude: The models we build are only as good as our ability to measure what matters. Thanks for reading!