From the article: "When tasked with coding, writing, editing, or summarizing, ask the user up to three targeted clarifying questions. Proceed with the task once you've received answers and understand the prompt fully. If the task is a simple factual question or conversational message, respond directly."
I started using similar approaches in the sonnet 3.5 era and found them incredibly useful at the time. The frontier lab models have gotten significantly better about their guesses over time, but I still sometimes turn to the technique if my own ideation is only about 80% of the way there, as the LLM's questioning can help me identify the blind spots that need more consideration.
Yes. I run local models, Qwen3.6-27B and IMHO the massive level up was the agents and skills files that I've worked on.
Basically I run a flow
Brainstorming > Create Spec > Review Spec* > Create Plans > Review Plan* > Execute Plan (in subagents) > Review Against Plan > Code Review* > Open PR > Finish Plan (marks plan files done)
* Each review step marked with an asterisk uses a paid larger LLM, right now Deepseek V4 Pro. Having it do this catches a lot of small things, and now I'm effectively one shotting any task I give it.
And it's not costing me much at all, just those three reviews. I could use a free model like Gemini but I'm happy with what I've got.
Isn't this akin to including all the (missing) keywords from the prompt? YMMV but to me we have found the less optimized way of using LLMs
From the article: "When tasked with coding, writing, editing, or summarizing, ask the user up to three targeted clarifying questions. Proceed with the task once you've received answers and understand the prompt fully. If the task is a simple factual question or conversational message, respond directly."
I started using similar approaches in the sonnet 3.5 era and found them incredibly useful at the time. The frontier lab models have gotten significantly better about their guesses over time, but I still sometimes turn to the technique if my own ideation is only about 80% of the way there, as the LLM's questioning can help me identify the blind spots that need more consideration.
I'm positively surprised such a little guidance makes such a difference.
is it also useful with the smaller (and cheaper) cloud models?
Yes. I run local models, Qwen3.6-27B and IMHO the massive level up was the agents and skills files that I've worked on.
Basically I run a flow
Brainstorming > Create Spec > Review Spec* > Create Plans > Review Plan* > Execute Plan (in subagents) > Review Against Plan > Code Review* > Open PR > Finish Plan (marks plan files done)
* Each review step marked with an asterisk uses a paid larger LLM, right now Deepseek V4 Pro. Having it do this catches a lot of small things, and now I'm effectively one shotting any task I give it.
And it's not costing me much at all, just those three reviews. I could use a free model like Gemini but I'm happy with what I've got.
Right on target