What you’ll learn
- Design and Build a Retrieval-Augmented Generation (RAG) System Understand how to integrate large language models (LLMs) with retrieval pipelines
- Implement Embeddings and Vector Databases for Semantic Search Learn how to generate and store embeddings using tools like OpenAI, ChromaDB, or Pinecone
- Develop an End-to-End AI Knowledge Assistant Build and deploy a functional AI chatbot using frameworks like LangChain, Streamlit, and FastAPI
- Evaluate and Optimize AI Performance Metrics Measure your assistant’s accuracy, relevance, and user experience using key performance metrics
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