Post by @datascibykashi

🤖 3 RAG PROJECTS YOU CAN BUILD Want to learn Retrieval-Augmented Generation (RAG) by actually building something? Don’t start with another basic “chat with PDF” demo. Build these 3 projects 👇 1️⃣ PERSONAL KNOWLEDGE ASSISTANT 📚 Difficulty: Beginner → Intermediate Build an AI assistant that can answer questions from your own: 📄 PDFs 📝 Notes 📚 Books 📊 Documents 💻 Markdown files Architecture: Documents ↓ Chunking ↓ Embeddings ↓ Vector Database ↓ Retriever ↓ LLM ↓ Answer + Sources 🛠️ Stack: Python • LangChain/LlamaIndex • Qdrant/Chroma • Sentence Transformers • LLM API • Streamlit 🔥 Add: ✅ Source citations ✅ Conversation memory ✅ Metadata filtering ✅ Document upload ✅ Reranking 2️⃣ RESEARCH PAPER RAG ASSISTANT 🔬 Difficulty: Intermediate Build an AI research assistant that can search and reason over academic papers. The system should: 📄 Ingest research papers 🔎 Retrieve relevant sections 🧠 Compare papers 📌 Extract findings 🔗 Provide citations 📊 Identify research gaps Architecture: Research Papers ↓ PDF Parsing ↓ Semantic Chunking ↓ Embeddings ↓ Vector + Keyword Search ↓ Reranking ↓ LLM ↓ Cited Answer 🔥 Advanced features: • Hybrid search • Cross-encoder reranking • Query rewriting • Paper metadata • Citation tracking • Multi-document comparison This starts looking like a real research tool, not just a chatbot. 3️⃣ AGENTIC RAG RESEARCH ASSISTANT 🤖 Difficulty: Advanced Now make your RAG system agentic. Instead of: Question → Retrieve → Answer Build: Question ↓ 🤖 Agent ↓ 🧠 Plan ↓ 🔎 Retrieve ↓ 📊 Evaluate Context ↓ 🔄 Search Again if Needed ↓ 🛠️ Use Tools ↓ 📝 Generate ↓ ✅ Verify ↓ 💬 Answer + Sources Give the agent access to: 🔎 Web search 📚 Vector database 🗄️ SQL database 🐍 Python tools 📊 Data analysis tools 📄 Document collections 🛠️ POSSIBLE STACK Frontend: React / Streamlit Backend: FastAPI Agent orchestration: LangGraph Vector DB: Qdrant Embeddings: Sentence Transformers Reranker: BGE / Cross-Encoder LLM: GPT / Claude / Gemini / open-source model Database: PostgreSQL Deployment: Docker + Cloud 🚀 BUILD THEM IN THIS ORDER Project 1: Learn RAG fundamentals ⬇️ Project 2: Learn advanced retrieval ⬇️ Project 3: Learn Agentic RAG + tool use By the end, you should understand: Chunking → Embeddings → Retrieval → Reranking → Generation → Evaluation → Agents → Deployment 💡 Don’t just build a RAG chatbot. Build a system that can retrieve, reason, use tools, verify information, and show its sources. That’s where RAG becomes much more interesting. 🔥 🔖 Save this if you’re learning RAG and AI Engineering. #RAG #AgenticRAG #GenerativeAI #creatorsearchinsights #datascience