𝗪𝗵𝘆 𝗗𝗼𝗺𝗮𝗶𝗻 𝗠𝗼𝗱𝗲𝗹𝘀 𝗠𝗮𝘁𝘁𝗲𝗿 𝗠𝗼𝗿𝗲 𝗶𝗻 𝘁𝗵𝗲 𝗔𝗜 𝗘𝗿𝗮

Software architecture is often a debate without a winner. You build one system. You never build the alternative under the same conditions. This makes every decision unfalsifiable. When a system fails, people blame the domain or the team. They rarely blame the architecture because there is no control group to compare it to.

We need a way to test our designs. We must separate essential complexity from accidental complexity. Essential complexity is the actual problem. Accidental complexity is the mess we create with our tools and processes.

AI makes implementation nearly free. This is a massive shift. In the past, the friction of writing code forced developers to create better structures. If your code was a mess, it became hard to manage. That pain was a feedback loop.

AI removes that friction. It can write messy, poorly structured code just as fast as clean code. The pain of a bad model no longer hits the developer during the build. Instead, the pain moves to production. You get corrupt data and impossible integration tasks.

A rich domain model is a tool to prevent this. It serves three specific jobs:

  • It helps you learn the domain by forcing you to give concepts shape.
  • It defines the domain so "what to build" is no longer a guess.
  • It documents the domain in code that stays updated via the compiler.

To work, a domain model must follow three rules:

  • Keep essential complexity whole. Do not scatter a single concept across hundreds of microservices.
  • Provide feedback. A wrong assumption should cause a compile error, not a silent bug.
  • Keep changes cheap. You should fix a concept in one place, not hunt for it in ten services.

If you split your domain too early into microservices to solve "bloat," you often just move the mess. You trade a single "god object" for a distributed mess that is harder to track. A dependency you cannot see is a dependency that will break in production.

The goal of a rich domain model is not to be right. The goal is to make being wrong visible while the cost of correction is still small.

Source: https://dev.to/leonpennings/what-is-the-reason-for-using-a-rich-domain-model-in-the-age-of-ai-3gg

Optional learning community: https://t.me/GyaanSetuAi