I am Building an AI Court with AI Judges, AI Lawyers.

Priyanshu Maity — Fri, 17 Jul 2026 07:52:00 +0000

Most legal AI tools focus on summarising documents or answering queries. While they’re useful, they rarely explain how a decision is reached or consider multiple perspectives before producing an answer.

I wanted to explore a different approach.

Instead of relying on a single language model,** I designed AI Courtroom—a multi-agent system** where specialised AI Lawyers argue both sides of a case, retrieve relevant legal information using Retrieval-Augmented Generation (RAG), challenge each other’s arguments, and present them to an AI Judge that evaluates the evidence before delivering a reasoned verdict.

In this article, I’ll explain the vision behind AI Courtroom, break down its architecture, and show how AI Judges, AI Lawyers, and Retrieval-Augmented Generation (RAG) come together to create an intelligent legal reasoning system.

The Vision Behind AI Courtroom

Artificial Intelligence has become remarkably good at answering questions, generating text, and summarising information. But in fields like law, the final answer is only part of the process. What truly matters is the reasoning behind that answer.

That idea became the foundation of AI Courtroom.

Instead of treating AI as a single chatbot that provides instant legal advice, I envisioned a system that simulates how a real courtroom operates—where multiple participants analyse the same case from different perspectives before reaching a conclusion.

In AI Courtroom, each AI agent has a specialised role. AI Lawyers represent opposing sides of a case, analyse the evidence, retrieve relevant laws and precedents using Retrieval-Augmented Generation (RAG), and build arguments to support their clients. Rather than agreeing with one another, they challenge claims, identify weaknesses, and present competing viewpoints.

An AI Judge then reviews the arguments, evaluates the supporting evidence, considers applicable legal principles, and delivers a reasoned verdict based on the information presented. The goal isn’t simply to produce an answer—it’s to make the decision-making process transparent and structured.

This architecture transforms AI from a question-answering assistant into a collaborative legal reasoning system. By combining specialised AI agents with reliable knowledge retrieval, AI Courtroom aims to reduce hallucinations, encourage balanced analysis, and provide explainable legal reasoning instead of one-sided responses.

While AI Courtroom is not intended to replace judges or lawyers, it explores how multi-agent AI can assist legal education, case analysis, and decision support by making complex legal reasoning more accessible, transparent, and interactive.

How I Became an AI Developer at 18

Priyanshu Maity — Fri, 17 Jul 2026 07:46:46 +0000

By Priyanshu Maity

If someone had told me a few years ago that I'd be spending most of my day building AI applications, I probably wouldn't have believed them.

I'm Priyanshu Maity, an 18-year-old AI Engineer and Full-Stack Developer from Kolkata, India. I'm still a student, and like most people my age, I'm figuring things out as I go. The difference is that somewhere along the way I became obsessed with artificial intelligence.

This isn't a story about becoming an overnight expert. It's about staying curious long enough to build something worthwhile.

It started with curiosity

I've always enjoyed understanding how things work.

At first, it was computers. Then programming. After that, web development. Every new topic opened the door to another one.

Eventually I discovered large language models and machine learning.

The idea that software could understand language, reason through problems, and help people solve real tasks completely changed how I looked at programming.

I wasn't interested in using AI just because it was trending. I wanted to understand what was happening behind the scenes.

I spent more time building than watching tutorials

One mistake I made early on was watching too many tutorials.

It feels productive, but after a while you realize you've learned how to follow instructions—not how to solve problems.

Things changed when I started building my own projects.

Some of them failed.

Some of them were messy.

Most of them were never published.

But every single one taught me something that another YouTube video couldn't.

Debugging your own code is where real learning starts.

Learning AI wasn't as complicated as I expected

When people hear "Artificial Intelligence," they often imagine complicated mathematics or research papers that only PhD students can understand.

That scared me too.

But I learned that you don't have to know everything on day one.

I started with the basics, experimented with APIs, read documentation, broke things, fixed them, and slowly began understanding how AI systems fit together.

Over time I became interested in areas like:

Generative AI
Machine Learning
AI Agents
Prompt Engineering
Automation
Legal Technology (LegalTech)

The more I built, the more confident I became.

Building real projects changed everything

There's a huge difference between saying you know AI and actually building something people can use.

Real projects force you to think about users, performance, edge cases, databases, APIs, and deployment—not just model outputs.

That's when programming became much more interesting to me.

I stopped chasing certificates and started chasing better projects.

Being 18 isn't an advantage or a disadvantage

One thing people often point out is my age.

Yes, I'm 18 years old, and I'm from Kolkata.

But once you're writing code, none of that really matters.

The compiler doesn't care how old you are.

Users don't care either.

They only care if what you build works.

I'm still learning

One thing I've realised is that technology moves too fast for anyone to know everything.

Every week there's a new model, framework, or tool.

Instead of trying to learn everything, I try to stay consistent.

Read.

Build.

Break things.

Fix them.

Repeat.

That approach has taught me far more than trying to memorise every new trend.

Looking ahead

I'm excited about where AI is going over the next few years, and I want to keep building products that solve real problems.

There's still a lot I don't know, and honestly, that's the part I enjoy the most.

Learning never really stops in software development.

And I hope it never does.

Thanks for reading.

I'm Priyanshu Maity, an 18-year-old AI Developer and Full-Stack Developer from Kolkata, India. I regularly share what I'm learning about AI, software engineering, and the projects I'm building.

If you're also learning AI, I'd love to hear what you're working on in the comments.

DEV Community: Priyanshu Maity