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DevelopmentIntermediate40 lessons18–22 hours
RAG & Knowledge Systems
Build retrieval-augmented generation systems that give AI accurate, up-to-date knowledge. Vector databases, embedding strategies, chunking, and evaluation.
What You'll Learn
Understand embeddings: how text becomes vectors and why it matters
Set up and query vector databases (Pinecone, Weaviate, Chroma)
Implement chunking strategies optimized for different document types
Build retrieval pipelines with hybrid search and reranking
Combine keyword search with semantic search for better results
Evaluate RAG quality with automated metrics and human review
Deploy production RAG pipelines with caching and monitoring
Integrate knowledge graphs with vector search for richer context
Outcomes
- Build production RAG systems with vector databases and embedding pipelines
- Implement advanced chunking and retrieval strategies for accuracy
- Evaluate and optimize RAG performance with systematic frameworks
- Deploy knowledge systems that handle enterprise-scale document collections
Prerequisites
- -Python fundamentals
- -Basic understanding of APIs
- -Familiarity with databases helpful
Projects You'll Build
- Build a document Q&A system with Pinecone/Weaviate
- Create a multi-source knowledge base with hybrid search
- Deploy a production RAG pipeline with evaluation metrics
Course Curriculum
Module 1: Embeddings & Vector Math
- 1.1What are embeddings and why they power modern AI search
- 1.2Embedding models compared: OpenAI, Cohere, Voyage, open-source
- 1.3Vector similarity: cosine, dot product, and Euclidean distance
- 1.4Dimensionality and its effect on retrieval quality
- 1.5Generating embeddings via API and locally with sentence-transformers
- 1.6Visualizing embeddings to understand your data
Module 2: Vector Databases
- 2.1Why you need a vector database (not just numpy)
- 2.2Pinecone: setup, indexing, querying, and namespaces
- 2.3Weaviate: schema design, hybrid search, and modules
- 2.4Chroma: the lightweight local-first option
- 2.5PostgreSQL pgvector: adding vectors to your existing database
- 2.6Choosing the right vector database for your use case
- 2.7Index types and performance tuning (HNSW, IVF, PQ)
Module 3: Chunking & Ingestion
- 3.1Why chunking strategy makes or breaks RAG quality
- 3.2Fixed-size chunking with overlap
- 3.3Semantic chunking: splitting by meaning, not characters
- 3.4Document-aware chunking: headers, paragraphs, code blocks
- 3.5Recursive chunking for hierarchical documents
- 3.6Metadata extraction: titles, dates, authors, sections
- 3.7Building ingestion pipelines for PDF, HTML, Markdown, and DOCX
Module 4: Retrieval Strategies
- 4.1Basic semantic search and its limitations
- 4.2Hybrid search: combining BM25 keyword search with vector similarity
- 4.3Reranking: using cross-encoders to improve result quality
- 4.4Multi-query retrieval: generating query variations for better recall
- 4.5Contextual compression: extracting relevant passages from chunks
- 4.6Parent document retrieval: small chunks for search, large chunks for context
Module 5: Evaluation & Optimization
- 5.1Why RAG evaluation is essential (garbage in, garbage out)
- 5.2Retrieval metrics: precision, recall, MRR, and NDCG
- 5.3Generation metrics: faithfulness, relevance, and completeness
- 5.4Building evaluation datasets from real user queries
- 5.5Automated evaluation with RAGAS and custom frameworks
- 5.6A/B testing retrieval strategies in production
- 5.7Common failure modes and how to diagnose them
Module 6: Production RAG Systems
- 6.1End-to-end RAG architecture for production
- 6.2Caching strategies: query cache, embedding cache, result cache
- 6.3Incremental indexing: updating knowledge without full reindexing
- 6.4Multi-tenant RAG: isolating knowledge per user or organization
- 6.5Knowledge graphs: adding structured relationships to vector search
- 6.6Monitoring retrieval quality in production with feedback loops
- 6.7Scaling RAG: handling millions of documents and concurrent queries
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