20 Days Complete
What You Have Learned
You have covered the complete RAG landscape — from theory to implementation, from basic retrieval to self-correcting advanced systems. Here is the full map of everything you now know.
✓Days 1–3 — What RAG is, why it matters, and how it compares to fine-tuning
✓Day 4 — The complete 4-step RAG architecture — Indexing through Generation
✓Days 5–7 — Document Loaders, Text Splitting, and the Recursive Text Splitter
✓Days 8–9 — Embeddings and Vector Stores — the core engine of RAG
✓Days 10–13 — Retrievers: basic, MMR, Multi Query, and Contextual Compression
✓Days 14–15 — Built a complete RAG system and mastered LangChain chains
✓Day 16 — Advanced RAG techniques used in production systems
✓Day 17 — Corrective RAG — self-correcting retrieval with evaluation and web search
✓Day 18 — Self RAG — four-checkpoint self-reflection and loop-based correction
✓Day 19 — Three-layer anti-hallucination strategy for production systems
What to Build Next
Project Ideas — Start Building Today
→YouTube Chatbot — Chat with any video — extend the Day 14 project
→Company Knowledge Base — Internal document Q&A chatbot for your team
→Personal Document Assistant — Chat with your own notes, books, and PDFs
→Research Paper Summarizer — RAG system over ArXiv papers
→Customer Support Bot — Product documentation powered support agent
RAG Cheat Sheet
Quick Reference
·Loader — PyPDF for PDFs, WebBase for websites, CSV for tabular data
·Splitter — RecursiveCharacterTextSplitter, chunk_size=1000, overlap=200
·Embeddings — OpenAI text-embedding-3-small — cost effective and high quality
·Vector Store — FAISS for development, Pinecone or Qdrant for production
·Retriever — Vector Store Retriever + MMR for diverse results
·Anti-Hallucination — Strict prompt + Is Supported check + better retrieval
·Advanced — CRAG for self-correction, Self RAG for maximum verified accuracy
What to Explore Next
Continue Your RAG Journey
✓LangGraph — Build agentic RAG systems with full control over the decision graph
✓RAG Evaluation — Use RAGas to measure faithfulness, relevancy, and answer quality
✓Multimodal RAG — Extend RAG to work with images and text together
✓Agentic RAG — Combine RAG with AI agents for dynamic autonomous retrieval
✦
In 20 days you covered the complete RAG landscape — theory, implementation, and advanced techniques. The only thing left is to build. Pick one project, implement it, iterate on it. Real learning happens through building, failing, and improving. You now have everything you need to build production-grade RAG systems.