10. COURSE RESOURCES & NEXT STEPS

(You've reached the end of the technical content. If you've built the projects, you know more about production RAG + KG systems than most engineers claiming to be "AI experts." The resources below help you go deeper. The career section shows you what's possible. But remember: the market doesn't care about courses completed - it cares about systems shipped. Build things, put them on GitHub, write about what you learned. That's how you get hired.)

Recommended Reading

Books:

• "Speech and Language Processing" - Jurafsky & Martin
• "Designing Data-Intensive Applications" - Martin Kleppmann
• "Graph Databases" - Ian Robinson, Jim Webber
• "Building LLM-Powered Applications" - Valentina Alto

Papers:

• "Attention Is All You Need" (Transformers)
• "BERT: Pre-training of Deep Bidirectional Transformers"
• "Retrieval-Augmented Generation for Knowledge-Intensive Tasks"
• "GraphRAG: Unlocking LLM discovery on narrative private data" (Microsoft)

Online Resources:

• Neo4j Graph Academy (free courses)
• Pinecone Learning Center
• LangChain Documentation
• OpenAI Cookbook