Mastering RAG: From Documents to AI Assistants

Level 1 — Foundations of RAG

• RAG vs Fine-tuning — when to use which
• Limitations of LLMs — hallucination & outdated knowledge
• RAG Architecture — Retriever + Generator
• Real-world use cases — chatbots & internal knowledge search

Level 2 — Embeddings & Vector Search

• What are embeddings & how they work
• Chunking strategies for documents
• Similarity search using cosine similarity
• Vector databases — FAISS & Chroma
• Hands-on: Convert documents → embeddings → retrieve relevant chunks

Level 3 — Building a Basic RAG Pipeline

• RAG pipeline flow end-to-end
• Context window management & token costs
• Prompt templating & context injection
• Tools: LangChain & OpenAI API
• Hands-on: Build a document-based Q&A system

Level 4 — Advanced Retrieval & RAG 2.0

• Hybrid search — keyword + semantic combined
• Re-ranking, metadata filtering & multi-query retrieval
• Query transformation, routing & agentic RAG concepts
• Hands-on: Improve baseline RAG accuracy

Level 5 — Evaluation & Debugging

• How to evaluate RAG systems end-to-end
• Evaluation frameworks — Ragas & TruLens
• Hallucination detection & retrieval metrics (precision & recall)
• Logging & tracing with LangSmith
• Hands-on: Debug wrong answers in a RAG pipeline

Level 6 — Productionizing RAG

• Running open-source models locally with Ollama & Hugging Face
• Scaling vector databases & caching strategies
• API deployment with FastAPI
• Security considerations for production RAG
• Hands-on: Deploy a live RAG API

Level 7 — Mini Project

• Build one end-to-end RAG application:
  1. Internal Knowledge Chatbot
  2. Resume Analyzer
  3. Customer Support Bot