Practical prompting, backed by research

The paradox: Everyone optimizes prompts for brevity, yet LLMs work better with long, detailed contexts. The ACE method lets you accumulate a structured knowledge base (a playbook) that improves the model's performance on complex tasks. The trick: don't rewrite the whole context from scratch, add small deltas — new strategies, common mistakes, edge cases as separate bullet points with IDs. The context grows like a reference manual instead of collapsing into generalizations. Result: the model picks what's relevant out of a 10-20k-token detailed playbook on its own, instead of working from a vague "be more careful."