RAG
- RAG Foundations
What RAG is and why it matters.
Traditional RAG architecture: data ingestion, parsing, embeddings, and retrieval.
Choosing and using vector databases effectively.
Building retrieval + generation workflows with LangChain.
- Advanced RAG Techniques
Advanced chunking strategies for precision retrieval.
Hybrid search: combining vector and keyword search.
Multimodal RAG for text, images, and more.
Persistent memory for context retention.
Self-RAG for improving retrieval quality.
Adaptive & Corrective RAG for dynamic and error-resistant pipelines.
- Agentic RAG Pipelines
Multi-agent architectures with LangGraph.
Designing agents for research, summarization, and decision-making.
Autonomous RAG with minimal human intervention.
Collaborative AI reasoning with specialized agents.
- LangSmith for RAG Evaluation & Optimization
Tracking and managing RAG experiments.
Debugging retrieval pipelines and fixing bottlenecks.
Running evaluation metrics to boost accuracy.
- Real-World RAG Projects
Chatbot with domain-specific knowledge.
Multi-agent research assistant for automated reports.
Multimodal AI assistant with text and image retrieval.
Deploying RAG applications to the cloud.
Who This Course Is For
AI developers & machine learning engineers.
Data scientists integrating retrieval systems.
Software developers building intelligent assistants.
Researchers exploring advanced RAG workflows.
Anyone aiming to master RAG from scratch to production-ready deployment.
Tools & Frameworks You’ll Master
LangChain – Build modular RAG pipelines.
LangGraph – Create advanced agent-based workflows with memory.
LangSmith – Track, debug, and evaluate RAG systems.
Vector Databases – FAISS, Pinecone, Weaviate, and more.
Cloud Deployment – Take AI apps from development to production.
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