Chuanxilu for Skilled Homo sapiens

Series: Classic Theory Meets Agent Practice (Part 1) TL;DR: A design review agent needs to find every issue AND avoid false positives. One agent can’t do both. Borrowing cascade retrieval from information retrieval — a 15-year-old method — I split it into two: a Recall Pass that casts a wide net, and a Precision Pass that filters strictly. Real defects get caught earlier, and the risk of rework during development drops. ...

Today’s Practice Pick a real multi-step task you have on hand, and hold a conversation with AI for at least 15 turns (one question + one answer = one turn). Example tasks: “Help me plan a family trip (destination, itinerary, budget, packing list)” “Help me set up a personal blog from scratch (pick a platform, choose a theme, write the first post)” “Help me analyze a career decision (take stock of where I am, pros and cons, action plan)” Don’t try to steer the conversation deliberately — let it unfold naturally. When the conversation reaches around turn 10, pause and send this message: ...

Series: Breaking to Build: TDD Process Iterations (Post 3) Post 1: What a Failed Experiment Got Right · Post 2: Using the Method to Improve the Method TL;DR: Phase 6 already does diagnostics at the integration level — drilling into each bug’s root cause. What it doesn’t do: cross-defect pattern scanning, component gap checking, execution order analysis. Those belong to Phase 7. In small systems, Phase 7 catches a few more bugs. As the system grows, those same three tasks produce something different — building test infrastructure, hardening CI rules, driving architectural evolution. Phase 7 doesn’t make architecture decisions. But it provides the scarcest input for those decisions: evidence-based problem localization. ...

📖 This is Part 3 of 5 in the “AI Path L0→L1 Upgrade Guide” series. Part 1: Understanding Your Tools · Part 2: From Vague Questions to Precise Instructions · Part 3: Turning AI Into Your Collaboration Partner · Part 4: Building Your Personal System (coming soon) · Part 5: Graduation & Next Steps (coming soon) TL;DR: Three core skills — follow-up iteration (the first answer is almost never the best), context management (periodic summaries, start fresh after ~20 turns, split complex tasks), and role-playing (assigning a role changes output depth). This week’s practice focus: deliberately run a 15+ turn long conversation and proactively do a progress summary. ...

Series: Breaking to Build: TDD Process Iterations (second post) Previous: What a Failed Experiment Got Right TL;DR: The TDD Pipeline taught “give principles, not steps” — but it had grown into a step-driven tool itself. I stripped the operational steps from Phases 1 through 5, keeping only principles, risk hints, and counterexamples. The model independently derived the steps I had deleted. Output quality held. The reason: Phases 1 through 5 are creative phases that need room to diverge. Removing the fixed track actually helped. The same strategy failed on Phase 6 — next post explains why. ...

Tip Card: Format Constraints Cheat Sheet Spend 10 seconds specifying a format in your prompt, save 10 minutes of reformatting afterward. Here are 6 of the most useful format constraints, each with a prompt template you can use right away. 1. Markdown Table Best for: structured information that needs comparison or summarization. “Output as a Markdown table with the following columns: Name, Description, Use Case, Notes” 2. Numbered List Best for: steps, key points, quick scanning. ...

Series: Breaking to Build: TDD Process Iterations (first post) TL;DR: I refined Phase 6 (pre-release testing) of the TDD Pipeline from step-driven to principle-driven. The goal was better output. I didn’t get it — the refined version was worse at drilling into individual bugs and building evidence chains. But comparing the two outputs revealed dimensional differences. The refined version was better at component gap checking and cross-bug pattern scanning. Those differences pointed to a judgment call: Phase 6 doesn’t need refining. It needs a layer on top of it. That layer later became Phase 7. ...

Tip Card: When Should You Ask AI to “Think Step by Step”? Adding “please reason step by step” at the end of your prompt — that’s Chain-of-Thought (CoT). Deceptively simple, yet remarkably effective in the right situations. The question is: when should you add it? The answer is straightforward. Watch for three signals. If any apply, add it. Signal 1: The Problem Requires Multi-Step Reasoning “If I save 30% of my monthly income at 4% annual interest, compounded, how much will I have after 10 years?” ...

We covered the RBGO (Role-Background-Goal-Output) framework in the previous post. But there’s a gap between knowing the framework and actually using it: how do you translate “I want…” into those four elements? Below are 5 common everyday scenarios. Each one starts with the vague version (what most people actually write), followed by the RBGO rewrite, and finally a breakdown of what changed and why. Scenario 1: Writing a Work Email Vague version: ...

TL;DR: Before-and-after comparison of the upgraded Why Articulation template, plus three transferable lessons: give principles not examples, lock critical steps with mandatory tone, and trust the model’s self-organization. Experiment limitations included. Series: Why Make AI Articulate Why Before Acting (Article 3) Previous: A 4-Variable A/B Test — Why Positive Examples Harm Prompt Performance Recap Article 1 started from Anthropic’s alignment research: teaching a model why rather than what cut misalignment from 22% to 3% (about 7×), and achieved equivalent results with 1/28 the data [1]. I adapted this into Why Articulation — a mechanism that forces AI to explain purpose, risks, and approach before writing any code. ...