This week
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Updated through the day. Every headline links straight to the source. The two lines underneath are ours.
Practical signal for code generation: models like Claude will rewrite more than necessary, and you can constrain this cheaply with a prompt instruction. The finding that extra reasoning budget and scale don't solve it is important—the issue is behavioral, not computational. If you're using LLMs for code repair, test this instruction in your pipeline.
The finding that a stronger teacher model can substitute for optimizer complexity is the practical takeaway: if you're running prompt optimization pipelines, simpler search loops with a good teacher may get you most of the way there. Worth testing against whatever GEPA-style tooling you've already built before investing in more elaborate search infrastructure.
The actionable finding here is negative and useful: don't let an LLM rewrite your coding prompts automatically, it measurably hurts output quality without buying anything back. If you're running coding agents at scale, standardizing prompt format to JSON is a cheap, evidence-backed lever worth testing against your own eval suite.
This names something anyone maintaining a CLAUDE.md or system prompt has felt but not measured: instructions accumulate because removing them feels riskier than keeping them, so context bloat compounds. The proposed fix, prompt comments that preserve rationale, is a cheap intervention worth testing on any long-lived agent prompt. If you're maintaining agent instruction files at scale, this gives you a concrete diagnosis and a starting point for pruning discipline.
Willison's link posts are usually a quick signal that something in the prompt engineering or agent tooling space is worth a second look. With no excerpt beyond the title, treat this as a pointer rather than a finished story: worth clicking through if you follow Crawshaw's agent work, otherwise low priority.
This remains one of the most-cited prompt engineering surveys because it catalogs the techniques (CoT, ReAct, self-consistency) rather than pitching any one of them. Anyone building prompting pipelines still uses this as a checklist before reaching for fine-tuning. Worth bookmarking even now as a baseline reference, though newer models have made some of the more elaborate tricks less necessary.