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RAG with Python Basic to Advance – Complete E-Book: Build Your Own Document Q&A Bot in Easy Hinglish (44 Pages, 23 Chapters)
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Do you want to build AI apps that answer questions from YOUR OWN documents — PDFs, notes, company files — instead of making things up? This complete, beginner-friendly e-book teaches you Retrieval-Augmented Generation (RAG) in easy Hinglish, from zero to a working Python Q&A bot. Every concept comes with real, tested Python code you can run yourself.
What's Inside the Book?
| Chapter | What You'll Learn |
|---|---|
| 1. RAG Kya Hai? | LLM ki 3 problems — knowledge cutoff, hallucination, private data — aur RAG unhe kaise solve karta hai |
| 2. LLM Basics Recap | Tokens, context window, aur "lost in the middle" problem |
| 3. RAG Architecture | Retrieve → Augment → Generate pipeline, offline indexing vs online query |
| 4. Documents Load & Clean | Text safai, metadata, citations ke liye tayyari |
| 5. Chunking Strategies | Fixed-size, recursive aur semantic chunking — tested code ke saath |
| 6–7. Embeddings | Embeddings kya hain + Python me TF-IDF aur transformer patterns |
| 8. Vector Database & Indexing | Flat search vs ANN indexes (IVF, HNSW), FAISS/Chroma/Pinecone guide |
| 9. Cosine Similarity | Scratch se numpy implementation, kyun cosine hi standard hai |
| 10. Apna Vector Store | Khud ka SimpleVectorStore class — add, search, metadata |
| 11. Retrieval Tuning | Top-k aur score threshold — "pata nahi" kab kehna hai |
| 12. Advanced Retrieval | MMR diversity, hybrid search, reranking — tested code |
| 13. Prompt Augmentation | Grounding rules, citations, context budget |
| 14. Generation | Poora RAG pipeline jodna, real LLM API pattern |
| 15. RAG Evaluation | Precision, recall, faithfulness — golden Q&A set ke saath |
| 16. Mini Project 1 | Text files par chalne wala Q&A bot |
| 17. Mini Project 2 | PDF par chalne wala Q&A bot (pypdf) |
| 18. Production Tips | Metadata filtering, caching, query rewriting, fallback, logging |
| 19. RAG Debugging Guide | 7 common problems, symptoms aur fixes |
| 20. Interview Questions | 18 real interview Q&A with answers |
| 21. Cheat Sheet + Glossary | Ek page me poori book + 20-term RAG glossary |
| 22. Learning Roadmap | RAG ke baad kya seekhein — 30-day plan |
| 23. Answers | Saare 71 practice questions ke detailed jawab |
Why You'll Love This
- ✅ 44 pages, 23 chapters — basic se advance tak, ek bhi page khaali nahi
- ✅ 71 practice questions with full answers + hands-on exercises har chapter me
- ✅ 6 custom diagrams — pipeline, chunking, embeddings, indexing, MMR, evaluation
- ✅ 2 complete mini projects — text-file Q&A bot aur PDF Q&A bot, dono ka code tested
- ✅ Har Python snippet executed & verified — copy-paste karke chala sakte ho, bina API key ke
- ✅ 18 interview questions with answers — job/internship tayyari ke liye
- ✅ Easy Hinglish — technical concepts simple bhasha me, real examples ke saath
- ✅ Chapter summaries — har chapter ka quick revision
Who Is This For?
- Python learners jo AI/LLM apps banana chahte hain
- Students preparing for AI/ML interviews
- Developers jo apne documents par chatbot/Q&A system banana chahte hain
- Freelancers jo clients ke liye RAG solutions offer karna chahte hain
Digital E-Book
- 📕 Format: PDF e-book (44 pages) — phone, tablet aur computer par khulta hai
- 📕 Language: Hinglish (Roman Hindi + English) — basic to advance
- 📕 Code: Saare Python snippets tested — numpy, scikit-learn, pypdf par based
- 📕 Delivery: Instant download link after purchase
Note: Ye ek digital e-book hai — koi physical book ship nahi hogi. Saare code examples bina paid API key ke chalte hain; real LLM API ka pattern alag se samjhaya gaya hai.
