Posts

📖 This is Part 4 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 · Part 5: Graduation & Next Steps Over three weeks you’ve picked up follow-up questions, context management, role-playing… the skills are piling up, and here’s the problem: how do you manage all these scattered abilities in one place? Week 4 is about exactly that: building a prompt library, choosing the right tools, and setting up knowledge management. Turning what you’ve learned into a personal system. ...

Series: Classic Theory Meets Agent Practice (Part 3) Part 1: Dual-Pass Review: Why You Can’t Have Both Recall and Precision · Part 2: Strategy Genes: Pruning Review Prompts with Genetic Algorithm Thinking TL;DR: Two controlled experiments. Code review dimensions went from 8 to 11, and known-issue detection went from 1/6 to 6/6. Design review introduced Axiomatic Design dimensions, and detection also went from 1/6 to 6/6. But the version with a math formula proved that more dimensions are not always better: computation consumed review attention, and findings dropped 35%. Run controlled experiments with known issues as reference, and you learn which dimensions actually work. ...

Yesterday’s discussion covered follow-up questions: three questions to dig out the hidden assumptions behind an AI answer. The catch is, you need to know what to ask. Some blind spots you simply can’t see from your own perspective. That’s when you give AI a different identity. The Devil’s Advocate Please act as a strict reviewer. Go through this plan point by point and identify every risk and weakness. Don’t hold back — the sharper, the better. ...

Series: Classic Theory Meets Agent Practice (Part 2) Previous: Dual-Pass Review: Why Recall and Precision Cannot Both Win Next: Dimension Experiments: Can a 36-Year-Old Book Fix Your Review Coverage? TL;DR: A review prompt went from 317 lines to 135 lines (-58%), and review quality improved by 29%. What I removed was not useful procedure, but redundant content the model could infer on its own. What stayed were strategy genes: irreplaceable constraints, negative examples, and tone locks. ...

The previous post was about how long conversations drift. After writing it, I noticed something else: drift does not only happen after a conversation gets long. It can also happen inside any answer that looks complete. AI answers quickly, and its conclusions often sound smooth. But it rarely says upfront: what assumptions does this conclusion depend on? If those assumptions are not checked, I end up accepting them by default. Accept enough unchecked assumptions, and the later analysis may be built on the wrong foundation. ...

The previous exercise was to run a 15-turn conversation with AI, using progress summaries and new conversations as checkpoints. If you actually did it, you probably noticed something else too: drift doesn’t always look the same. The three cases below are all failures I’ve run into myself. Here’s what happened, why it happened, and how to avoid it. Failure 1: Work Directions Got Mixed Together What Happened I was figuring out the approach for a project. I first discussed Approach A with AI, building a data dashboard. After 4 turns, it didn’t feel deep enough, so I switched to Approach B, automated reports, for another 3 turns. Then I thought maybe I could combine Approach C’s real-time push capability. Three directions kept jumping around in the same conversation for a dozen turns. ...

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. This series is about one thing: how classic theoretical frameworks directly guide AI agent engineering. The first post starts with cascade retrieval, a 15-year-old method from information retrieval (IR), and the much older Recall vs. Precision tradeoff it sits on top of. The 1966 Cranfield II experiments proved these two goals fight each other. Applying that old problem’s newer solution to design review made a striking difference. ...

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)” Let the conversation unfold naturally without forcing strict structure at first, but do pause around turn 10 and run the progress-summary checkpoint. 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 · Part 5: Graduation & Next Steps 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. ...