- Home
- Courses
- Artificial Intelligence
- Retrieval Augmented Generation (RAG): From Theory to Production
Curriculum
- 8 Sections
- 67 Lessons
- Lifetime
Expand all sectionsCollapse all sections
- 1. RAG Foundations and Architecture8
- 1.1BEWX 1.1 What is Retrieval Augmented Generation and why it matters
- 1.2BEWX 1.2 Problems RAG solves – hallucinations, knowledge cutoff, domain specificity
- 1.3BEWX 1.3 RAG architecture overview – retrieval and generation components
- 1.4BEWX 1.4 Comparing RAG vs fine-tuning vs prompt engineering
- 1.5BEWX 1.5 Real-world applications of RAG systems
- 1.6BEWX 1.6 Basic RAG workflow – step-by-step
- 1.7BEWX 1.7 Advantages and limitations of RAG approach
- 1.8BEWX 1.8 Setting up your development environment for RAG
- 2. LLM and Embedding Foundations8
- 2.1BEWX 2.1 Introduction to Large Language Models (LLMs)
- 2.2BEWX 2.2 How LLMs process and generate text
- 2.3BEWX 2.3 Understanding tokens and tokenization
- 2.4BEWX 2.4 Embeddings explained – converting text to vectors
- 2.5BEWX 2.5 Different embedding models – OpenAI, open-source alternatives
- 2.6BEWX 2.6 Semantic similarity and cosine distance
- 2.7BEWX 2.7 Choosing the right LLM for your RAG system
- 2.8BEWX 2.8 API-based vs open-source LLM options
- 3. Information Retrieval Techniques9
- 3.1BEWX 3.1 Information retrieval overview and concepts
- 3.2BEWX 3.2 Keyword-based search – TF-IDF and BM25
- 3.3BEWX 3.3 Implementing BM25 for document retrieval
- 3.4BEWX 3.4 Semantic search using embeddings
- 3.5BEWX 3.5 Vector similarity search algorithms
- 3.6BEWX 3.6 Hybrid search – combining keyword and semantic
- 3.7BEWX 3.7 Metadata filtering and structured queries
- 3.8BEWX 3.8 Query expansion and reformulation
- 3.9BEWX 3.9 Evaluating retrieval quality – metrics and benchmarks
- 4. Vector Databases and Storage8
- 4.1BEWX 4.1 Introduction to vector databases
- 4.2BEWX 4.2 Popular vector databases – Pinecone, Weaviate, Milvus, Qdrant
- 4.3BEWX 4.3 PostgreSQL with pgvector extension
- 4.4BEWX 4.4 Indexing strategies for vector databases
- 4.5BEWX 4.5 Approximate Nearest Neighbor (ANN) algorithms
- 4.6BEWX 4.6 Scaling vector databases for production
- 4.7BEWX 4.7 Vector database comparison and selection guide
- 4.8BEWX 4.8 Hands-on – Setting up and querying a vector database
- 5. Data Preparation and Chunking8
- 5.1BEWX 5.1 Document loading – PDFs, HTML, markdown, code
- 5.2BEWX 5.2 Text extraction and cleaning
- 5.3BEWX 5.3 Chunking strategies – fixed size, semantic, token-based
- 5.4BEWX 5.4 Recursive character splitting
- 5.5BEWX 5.5 Chunk overlap and context preservation
- 5.6BEWX 5.6 Handling different document types
- 5.7BEWX 5.7 Preprocessing and normalization
- 5.8BEWX 5.8 Creating metadata for chunks
- 6. Building and Evaluating RAG Systems10
- 6.1BEWX 6.1 Building a simple RAG system – step-by-step
- 6.2BEWX 6.2 Query processing pipeline
- 6.3BEWX 6.3 Retriever implementation
- 6.4BEWX 6.4 Prompt engineering for RAG context
- 6.5BEWX 6.5 Handling hallucinations and grounding responses
- 6.6BEWX 6.6 RAG evaluation metrics – relevance, faithfulness, correctness
- 6.7BEWX 6.7 Using evaluation frameworks (Ragas, LangSmith)
- 6.8BEWX 6.8 Measuring retrieval quality
- 6.9BEWX 6.9 Measuring generation quality
- 6.10BEWX 6.10 A/B testing RAG configurations
- 7. Advanced RAG Techniques9
- 7.1BEWX 7.1 Query rewriting and expansion
- 7.2BEWX 7.2 Reranking retrieved documents with cross-encoders
- 7.3BEWX 7.3 Multi-hop reasoning and complex queries
- 7.4BEWX 7.4 Agentic RAG – agents making retrieval decisions
- 7.5BEWX 7.5 Graph-based RAG systems
- 7.6BEWX 7.6 Multimodal RAG – combining text and images
- 7.7BEWX 7.7 Time-aware and temporal RAG
- 7.8BEWX 7.8 Handling uncertainty and confidence scores
- 7.9BEWX 7.9 Implementing filters and constraints
- 8. RAG Production and Optimization7
- 8.1BEWX 8.1 Deploying RAG systems to production
- 8.2BEWX 8.2 Logging and monitoring RAG systems
- 8.3BEWX 8.3 Cost optimization and latency reduction
- 8.4BEWX 8.4 Caching strategies for retrieval
- 8.5BEWX 8.5 Real-time RAG and streaming responses
- 8.6BEWX 8.6 Security and data privacy in RAG
- 8.7BEWX 8.7 Scaling RAG for enterprise use
Modal title
Main Content