Full-stack LLM Engineering Lab. Features: Autonomous Agents (ReAct/AutoGPT) | Fine-Tuning Llama/Mistral (SFT/DPO) | Large Model Deployment (DeepSeek 671B / 2.5-bit) | Advanced RAG (Hybrid Search) | Function Calling (Stream/Text-to-SQL/External APIs) | Frameworks (LangChain, Semantic Kernel, OpenAI) | Daily SOTA Paper Tracking. From theory to 0-to-1

Built a Retrieval-Augmented Generation (RAG) chatbot using LangChain and OpenAI models.

Smart text chunker for LLM preprocessing (sections → paragraphs → sentences → hard splits).

This course is designed to teach you how to QUICKLY harness the power of the LangChain library for LLM applications. Build 3 end-to-end working LangChain based generative AI applications with no fluff, no toy examples - just real projects using real APIs and real-world skills.

This is a comprehensive Retrieval-Augmented Generation (RAG) system built with Gemini 2.5 Flash and Qdrant. It features a modular multi-page Streamlit interface for seamless configuration, document ingestion, and intelligent chatting.

A robust, production-grade pipeline converting complex Medical PDFs into structured, RAG-ready JSONL datasets. Features smart table merging, multimodal extraction, and dynamic layout analysis using Detectron2 & PaddleOCR.