Dogfooding

TL;DR

Most companies test their products extensively before launch: focus groups, QA cycles, beta programs, the whole nine yards. But the some do something that sounds almost too simple: they use the thing themselves. When your engineers debug code with the debugging tool they built, when your sales team manages leads in the CRM they're shipping next month, you cut 1 layer. If it's not good enough for the people who understand it best, it's definitely not ready for everyone else.

What you need to know

In the 1970s, Alpo commercials featured actor Lorne Greene mentioning that he fed Alpo to his own dogs. The tech world grabbed this concept and ran with it, with Microsoft coining "eating our own dogfood" in 1988 when manager Paul Maritz sent an internal email encouraging employees to use Microsoft products in their daily work.

The ideal setup is not just test the product: they should depend on it. When Slack's teams communicate through Slack eight hours a day, their designers become unwilling beta testers for every feature update. They experience every friction point, every confusing workflow, every performance hiccup that would otherwise become customer complaints. That's different from sitting someone down for an hour-long usability session.

The feedback comes loaded with context. When GitLab's teams use their AI features for code reviews and documentation, bugs get reported with technical precision and fixed within sprint cycles.

This creates several key advantages:

• Immediate context: Problems get reported by people who understand both the technical constraints and the intended user experience
• Real-world usage patterns: Your team discovers edge cases and workflow combinations that formal testing misses
• Faster iteration cycles: Fixes happen within sprints rather than after customer escalations

Compare that to waiting for customer bug reports after launch, when you're scrambling to push emergency patches while your support team fields angry emails.

The approach has limitations. Your employees are biased toward features they helped build and workflows they understand. They're also typically more technical than your normal users, so they'll tolerate complexity that would frustrate customers. Keep that in mind.