The bitter lesson of LLM evals
parsed.com · 2,646 words · saved by 2 readers
Supercharge, interpret and evolve your AI systems.
The bitter lesson of LLM evals Position July 13, 2025 The bitter lesson of LLM evals Turning expert judgment into a compounding moat. Because in LLM evals, scaling care beats scaling compute. Authors Affiliations Charles O'Neill Parsed Mudith Jayasekara Parsed Max Kirkby Parsed In the history of artificial intelligence, there is a famous principle known as the Bitter Lesson. Coined by Rich Sutton, it observes that the biggest gains in performance have consistently come not from elaborate, human-designed knowledge, but from general methods that leverage massive increases in computation. The les
saved by
related reading
- [2412.00543] Evaluating the Consistency of LLM Evaluatorsarxiv.org
- What We’ve Learned From A Year of Building with LLMs – Applied LLMsapplied-llms.org
- [2510.13786] The Art of Scaling Reinforcement Learning Compute for LLMsarxiv.org
- Your Evals Will Break and You Won't See It Coming - Lun Wangwanglun1996.github.io
- Your AI Product Needs Evals – Hamel's Blog - Hamel Husainhamel.dev
- Lil'Loglilianweng.github.io
- Does the Bitter Lesson Have Limits?dbreunig.com
- The Bitter Lesson is Misunderstoodobviouslywrong.substack.com
- Patterns for Building LLM-based Systems & Productseugeneyan.com
- Animals vs Ghosts – karpathykarpathy.bearblog.dev
- Will scaling work?dwarkeshpatel.com
- The Bitter Lesson is coming for Tokenization – ⛰️ lucalplucalp.dev