The Bitter Lesson

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1. 01

   Do not pack your knowledge into rules the model will outgrow: build the agent so that a better model makes it better, not broken.

   The Bitter Lesson · 2019

   An agent's scaffold must be thin enough that a better model improves it rather than breaks it.

   Sutton's argument: hand-coded human knowledge wins in the short run, then loses to search and learning that scale with compute. For agent builders, thick scaffolding — rigid step lists, fixed plans, parsers for the model's exact wording — is the human-knowledge bet, and it breaks at the next model release. Better: give the model tools, the goal and the constraints, verify the outputs, and keep the scaffold thin. Where determinism matters (compliance, money), keep it outside the model as a workflow, not inside it as a prompt. The "bitter" part: the cleverest prompt engineering has a shelf life of one model generation.

   “The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin.”

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