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- Limitations of LLMs — hallucination & outdated knowledge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:
- Internal Knowledge Chatbot
- Resume Analyzer
- Customer Support Bot
