The Prodinja Blog
Page 3 of 50 — deep-dive frameworks, honest analysis, and actionable playbooks for product managers.
JTBD vs Personas: Which One Actually Predicts Behavior?
Personas describe who your customer is; jobs describe what they're trying to do, and only one reliably predicts a purchase.
From Jobs to User Stories: Bridging JTBD and Your Agile Backlog
User stories describe solutions in disguise; anchor them to jobs so your backlog stays about progress, not pixels.
JTBD in B2B vs B2C: Why the Same Framework Behaves Differently
In B2C one person holds the job; in B2B a committee holds five conflicting ones, and that changes everything downstream.
Jobs-to-be-Done Meets the Journey Map: Two Lenses, One Experience
JTBD tells you the job; the journey tells you how it feels to get it done — how to combine both without muddling either.
LLM-as-Judge: When to Trust a Model to Grade a Model
Model-graded evals scale beautifully and lie confidently — learn exactly when an LLM judge is trustworthy and when it isn't.
The Anatomy of a Context Packet: Five Decisions Every AI Feature Forces You to Make
Every AI answer is assembled from five choices - include, exclude, retrieve, budget, guard - make them on purpose.
Push, Pull, Anxiety, Habit: The Four Forces Behind Every Switch
Progress is a tug-of-war. Learn to diagnose the two forces pulling customers toward you and the two holding them back, straight from their own words.
Build, Buy, or Configure: Choosing How to Automate a Process
Before you build a bespoke workflow engine, ask whether a configured platform gets you 80% at a tenth of the maintenance.
P99 Is the Product: Tail Latency and Perceived Speed in AI UX
Median latency lies—your slowest 1% of requests defines the experience, and streaming can rescue it. Here's how to measure what users feel.
Side Projects That Prove You Can PM (and the Kind That Waste Your Time)
Not every side project impresses a hiring manager; here's how to pick one that demonstrates discovery, trade-offs, and shipping discipline.
Working With Data Scientists: Why Their Experiments Aren't Your Features (Yet)
Data scientists run experiments with unknown outcomes; PMs commit to roadmaps with dates. Here's how to bridge the two without breaking trust.
Fraud Is a Product Problem: Protecting Both Sides of the Market
Fake accounts, collusion, and payment fraud quietly tax every marketplace. The PM's frame for fighting it without killing conversion.
Data Clean Rooms Explained: A Product Manager's Working Model
Clean rooms promise privacy-safe data collaboration, but PMs need to know what they actually compute and what they deliberately hide.
Agent Assist Done Right: Where AI Belongs in Telecom Support
AI that summarizes and suggests can help agents or bury them in wrong answers; here's how PMs scope support copilots that earn trust.
Mobile Product Management: Platform-Specific Considerations
Mobile PM adds review cycles, platform guidelines, and performance constraints desktop PMs don't face.
Soft Delete vs Hard Delete: What 'Deleted' Should Really Mean in Your Schema
Does 'delete' mean gone forever or just hidden? The answer is a product, legal, and schema decision all at once.
Traces, Not Logs: Making Agent Behavior Observable
A flat log tells you what happened; a trace tells you why the agent decided it — and that's what you'll need at 2am.
A PM's Framework for Choosing the Right Model for a Feature
Stop defaulting to the newest model — a five-factor framework to match the model to the job, budget and risk.
Rate Limiting as a Pricing Lever, Not Just a Safety Valve
Your rate limits already segment customers by willingness to pay. Design them on purpose instead of by accident.
Cost-to-Serve by Segment: Which Customers Quietly Kill Your AI Margin
Blended margin hides the truth. Segment cost-to-serve and you will find whales, minnows, and customers you should reprice.
PM Skills Assessment: A Framework for Honest Self-Evaluation
Where are you strong? Where are you weak? Most PMs can't answer this honestly without a framework.
The JSON Column Temptation: When Semi-Structured Data Helps and When It Hides Debt
Dumping flexible data into a JSON column feels fast, but it can quietly become an un-queryable, un-validated mess.
Designing a Sandbox: The Test Environment That Determines Whether Developers Ever Go Live
Developers won't ship against your API until they can safely break it—design a sandbox with test data, test credentials, and parity.
When Design and Engineering Disagree: The PM as Trusted Arbiter
A method for resolving design-versus-feasibility standoffs that keeps both partners' trust and lands a real decision.