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Introduction to Retrieval-Augmented Generation and vector databases
Build a RAG system that can answer questions from a collection of documents. Use at least 10 documents and implement proper chunking, embedding, and retrieval.
Deliverable: Working RAG system + documentation + test queries
Implement the same RAG pipeline using Pinecone and ChromaDB. Compare performance, ease of use, and results quality.
Deliverable: Comparison report + code for both implementations
Choose your AI Second Brain project scope: personal knowledge base, company documentation system, or research assistant. Create initial design document.
Hybrid search, reranking, and multi-document synthesis
Advanced retrieval strategies, query optimization, hybrid search combining vector and keyword search...
Building your AI second brain with note-taking integration
Notion, Obsidian integration, knowledge graph construction, personal memory systems...
Aggregating knowledge from emails, notes, documents, and web
Cross-source data aggregation, unified knowledge interface, intelligent search...
Company-wide documentation and knowledge bases
Enterprise RAG deployment, access control, security, compliance...
Research assistants, code documentation, legal systems
Specialized RAG applications for different industries and use cases...
Performance tuning, cost optimization, production deployment
Performance optimization, caching strategies, cost management, scaling...
Complete AI Second Brain deployment and presentation
Due: Tuesday of Week 29
Complete AI Second Brain system with: RAG implementation, multi-source integration, intelligent search, user interface, documentation, and demonstration of real-world usage.
Create intelligent knowledge systems that never forget
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