AI Engineer | GenAI, LLMs, RAG | Building real-world intelligent solutions

Tarun S | Linktree

WeGPT is a collaborative chat platform powered by LLMs that allows multiple users to share and interact in the same chat session, each retaining their individual conversation history. Designed with real-time collaboration in mind, it merges RAG (Retrieval-Augmented Generation), vector databases, and cloud-native deployment.

💡 Key Features
Multi-User Collaborative Chat
Share a chat session with others and view contextual responses with complete message continuity. Each user's contributions are tracked while maintaining shared LLM context.

Persistent Chat History via VectorDB
Conversations are embedded using Sentence Transformers and stored in ChromaDB, enabling semantic retrieval of historical context during multi-turn chats.

LLM Integration via Groq API
Backend connected to Groq-powered LLMs for fast and contextually aware responses.

RAG-Powered Retrieval
When responding, the system uses top-N similarity search from ChromaDB to retrieve relevant past exchanges.

🤝 WeGPT – Collaborative LLM Chat with Shared Context

LegalMate is a fine-tuned open-source LLM designed to specialize in answering India-based legal queries, particularly related to topics such as IPC, CrPC, Contract Law, and more. This project demonstrates the practical impact of LoRA-based finetuning on a base model like Mistral, making it more domain-aware, accurate, and relevant in a specific use case.

🔍 What It Does
Improves Legal Understanding
Fine-tunes the Mistral LLM on curated legal Q&A data using QLoRA, enhancing the model’s ability to generate more accurate, jurisdiction-specific answers.

Contextual Q&A Accuracy
Compares baseline model performance vs fine-tuned performance on structured prompts to show improvements in relevance, tone, and accuracy.

Low-Cost Finetuning
Utilizes QLoRA (Quantized LoRA) to reduce GPU memory usage, allowing training on a Google Colab free tier GPU (T4) with minimal compute resources.

⚖️ LegalMate – Domain-Specific LLM Finetuning for Legal Q&A

Why: Business teams needed quick, self-serve dashboards without relying on BI teams.
What: Created a GenAI interface for uploading Excel datasets and receiving auto-generated dashboards based on data insights.
How:

Used LLMs (Mistral, Llama 3.2, Phi 3.5) for data interpretation and insight generation.

Converted prompts into .pbit Power BI templates using backend logic with compression/decompression utilities.

Hosted via Docker on Azure; built frontend in Streamlit.
Impact: Reduced dashboard creation time by 60%, enabling wider adoption of Power BI by non-technical teams.

📊 NLP to Dashboard – GenAI-Powered Dashboard Generator 

Developed a full-stack GenAI-powered agentic system to automate the recruitment lifecycle for enterprise hiring teams, driven by a real business need to reduce manual load and improve match accuracy.

💡 Why: Recruiters spent 10+ hours/week manually screening profiles and refining JDs. The system was built to reduce this effort while improving decision-making accuracy.

⚙️ How:

JD Generator Agent – LLM-based generator using Azure OpenAI to create and refine JDs from hiring prompts.

Smart Sourcing Agent – API + RPA-based agent that posts jobs externally and tracks applicants.

Resume Matcher Agent – NLP + IDP engine extracts key skills and compares resumes using Sentence Transformers + ChromaDB.

Interview Scheduler Agent – Automates interview slotting and captures stakeholder feedback.

🔧 Backend: FastAPI, LangChain Agents, Docker, Azure

📈 Impact: Reduced JD drafting time by 80%, sourcing effort by 60%, and screening time by 50% during internal PwC 

🧠 Agentic Talent Search Platform

Tarun S

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