llm

🚀 Built a Basic RAG and an Advanced RAG System from Scratch — Without LangChain

To understand how RAG works internally, I first built a Basic RAG Pipeline: Basic RAG PDF → Extraction → Chunking → Embeddings → FAISS Search → Top-K Chunks → Prompt → LLM → Answer It works well for simple questions, but struggles with exact keywords, IDs, and complex queries. So I built an Advanced RAG Pipeline: Advanced RAG PDF → Extraction → Chunking → Embeddings → Dense + Sparse Index → Query Understanding → Dense Retrieval + Sparse Retrieval → Reciprocal Rank Fusion (RRF) → Cross-Encoder Reranking → Noise Filtering → Context Fusion → Prompt → LLM → Answer Key Improvements ✅ Semantic Search (FAISS) ✅ Exact Keyword Search (BM25) ✅ Hybrid Retrieval ✅ Cross-Encoder Reranking ✅ Duplicate Removal ✅ Better Context Selection ✅ Reduced Hallucinations One of the biggest lessons from this project: A better RAG system isn't always about using a better LLM. It's often about building a better retrieval pipeline. Tech Stack: Python • Streamlit • FAISS • Sentence Transformers • BM25 • Cross-Encoder Reranker • Groq #RAG #GenAI #LLM #AIEngineering #MachineLearning #Python #FAISS #BuildInPublic