LLM Course – Build a Semantic Book Recommender

LLM COURSE – BUILD A SEMANTIC BOOK RECOMMENDERPython, OpenAI, LangChain, Gradio

Ever wondered how AI-powered book recommendation systems work? Want to go beyond simple keyword searches and build a **semantic book recommender** that actually understands books?

This book takes you on an exciting and practical journey into the world of **LLMs (Large Language Models), embeddings, and vector search**—but with a twist. You won’t just be reading dry academic explanations. Instead, you’ll follow **Anya Sharma**, a recent CS graduate who, thanks to a mix of ambition and spite, decides to build an AI recommender to out-code her overconfident classmate, Marcus.

Through **hands-on coding, humor, and real-world AI applications**, you will:

• Master **embeddings and vector search** to find similar books based on content, not just keywords.
• Use **OpenAI’s API** to generate intelligent recommendations.
• Implement **FAISS** for real-time, scalable search.
• Develop a functional **Gradio UI** that makes AI look pretty (or at least usable).
• Handle AI hallucinations, optimize queries, and avoid dumb mistakes that cost money.

If you've ever struggled with AI projects, debugging nightmares, or just want a **fun and practical guide to semantic search**, this book is for you.

What You’ll Learn (Without Losing Your Mind):

• **How embeddings work:** Stop guessing and start understanding how AI represents text in multi-dimensional space.
• **The power of cosine similarity:** Discover why AI can compare books based on meaning.
• **LLMs in action:** Summon GPT-4 like a dark sorcerer and make it analyze books intelligently.
• **Optimizing AI for cost and performance:** Because nobody wants a recommender that bankrupts them.

This isn’t just another AI book. It’s a **practical, entertaining, and slightly chaotic** ride through building a real-world **semantic book recommender**—one that doesn’t suck.