class Ujjwal:
role = "Computer Engineering Student"
specialization = "Artificial Intelligence"
interests = [
"Generative AI",
"RAG",
"LLMs",
"NLP",
"Machine Learning",
"Data Science"
]
currently_building = "LegalRAG"
philosophy = "Learn → Build → Break → Fix → Repeat"
status = "🚀 Building things that solve real problems"Build practical AI systems instead of just training notebooks.
Right now I'm diving deep into Retrieval-Augmented Generation (RAG) and building systems that can actually retrieve evidence, reason over documents, and generate grounded answers.
An AI-powered legal document intelligence system.
📄 Legal Documents
│
▼
┌─────────────────┐
│ Document Parser │
└────────┬────────┘
▼
🧩 Semantic Chunking
│
▼
🧠 Embeddings
│
▼
🔎 Vector Search
│
▼
📚 Retrieval
│
▼
🤖 LLM
│
▼
┌──────────────────┐
│ Grounded Answer │
│ + Source Citation│
└──────────────────┘
Semantic Chunking · Embeddings · Vector Search · Reranking
LLMs · Prompt Engineering · Citation Verification
RAG Evaluation · Multi-document Retrieval
Scikit-learn LangChain RAG NLP LLMs
NumPy Pandas Matplotlib Power BI Tableau
|
AI × Legal Documents × RAG An evidence-grounded system for querying and analyzing legal documents. Core:
|
ML × Demand Prediction Predicts product demand and helps optimize inventory decisions using historical data. Core:
|
|
AI × E-Commerce An intelligent shopping assistant designed to help users discover and evaluate products. Core:
|
AI × Career Intelligence Analyzes skills and interests to help users explore possible career paths. Core:
|
┌───────────────┐
│ GENERATIVE │
│ AI │
└───────┬───────┘
│
┌───────▼───────┐
│ LLMs │
└───────┬───────┘
│
┌──────────────▼──────────────┐
│ RAG │
└──────────────┬──────────────┘
│
┌──────────────▼──────────────┐
│ Retrieval + Embeddings │
└──────────────┬──────────────┘
│
┌──────────────▼──────────────┐
│ Production AI Systems │
└─────────────────────────────┘
| 🎓 Education | 🤖 Focus | 🏆 Experience |
|---|---|---|
| BTech Computer Engineering | AI / ML | Hackathons |
| AI Specialization | RAG / LLMs | Technical Events |
| CGPA 9.26 | NLP / Data Science | Open Source |
+ Build production-grade AI applications
+ Master RAG & LLM architectures
+ Learn advanced retrieval & evaluation
+ Explore AI Agents
+ Improve MLOps & deployment
+ Contribute to open source
+ Build projects worth talking about
