惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

aimingoo的专栏
aimingoo的专栏
Engineering at Meta
Engineering at Meta
有赞技术团队
有赞技术团队
博客园_首页
Apple Machine Learning Research
Apple Machine Learning Research
Vercel News
Vercel News
G
Google Developers Blog
Blog — PlanetScale
Blog — PlanetScale
IT之家
IT之家
MongoDB | Blog
MongoDB | Blog
Y
Y Combinator Blog
B
Blog
The GitHub Blog
The GitHub Blog
M
MIT News - Artificial intelligence
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
Stack Overflow Blog
Stack Overflow Blog
C
Check Point Blog
Microsoft Azure Blog
Microsoft Azure Blog
D
DataBreaches.Net
I
InfoQ
Recent Announcements
Recent Announcements
阮一峰的网络日志
阮一峰的网络日志
腾讯CDC
H
Help Net Security

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
Your PM Retrospectives Are Lying to You
Tongshan · 2026-05-23 · via DEV Community

Tongshan

Every quarter, product teams hold retrospectives. Someone asks "what went well?" and "what didn't?" The team lists bullet points. Someone writes "we should communicate better." Everyone nods. Nothing changes.

The problem isn't the retro format. It's that you never defined what success looked like before the work started — so you can't actually evaluate anything now.

I've run product teams at iQIYI, NIO, and Alibaba. The retrospectives that mattered had one thing in common: a pre-written success criterion that existed before any work began. Every other retro was theater.

The retrospective trap

When you define success after seeing the results, you unconsciously fit the definition to the outcome. If engagement went up 5%, that was the goal. If it went down 5%, you pivot to "we learned a lot." The retro becomes a narrative exercise, not a learning exercise.

Real learning requires a pre-mortem question: "What would we need to see to know this worked?"

That question, answered before the sprint starts, is the only thing that makes a retrospective honest.

The four questions that fix retrospectives

Before any significant initiative, write answers to these four questions:

1. What is the decision we're making?
Not "build feature X." The actual decision: "Are we betting on engagement-led growth over acquisition-led growth this quarter?" Name it. A decision that can't be stated in one sentence is a decision you haven't made yet.

2. What does success look like — exactly, in advance?
One sentence. Measurable. Time-bound. Not "improve retention" but "increase D30 retention from 22% to 28% by July 15." If you can't write this before you start, you're not ready to start.

3. What's the killer assumption?
Every initiative rests on one belief that, if wrong, invalidates the whole effort. Find it. Name it. Write it down. "We're assuming that power users are churning because of the onboarding flow, not the core product." If that's wrong, no amount of onboarding optimization saves you.

4. When is the assumption review date?
Not the ship date — the date you'll check whether the assumption held. Six weeks post-launch, you open the document and ask: did the metric move? Did the assumption prove correct? This is the only retrospective question that matters.

What honest retrospectives look like

With pre-written criteria, your retro becomes a one-page debrief:

  • Did the metric hit the target? (Yes/No, with data)
  • Did the killer assumption hold? (Yes/No, with evidence)
  • What does this tell us about the next decision?

That's it. No "communication" bullet points. No vague learnings. Just a clear verdict on whether the bet you made was right, and what you now know that you didn't before.

The teams that compound the fastest aren't the ones that ship fastest. They're the ones that learn fastest — and learning requires knowing what you were trying to prove before you tried to prove it.

I built a complete 4-step decision framework around this: https://dcljoyful.gumroad.com/l/toPM