Build enterprise-grade conversational AI, Retrieval-Augmented Generation (RAG) pipelines, vector search, and custom chat UIs.
Create high-accuracy, hallucination-resistant conversational interfaces. Learn Retrieval-Augmented Generation (RAG), semantic vector embedding search, hybrid keyword/vector search, session memory management, guardrails, and real-time streaming WebSocket/SSE frontends.
12 weeks · 48 guided hours · 4 hours / week · ₹9,999
Syllabus
- Conversational AI Foundations & LLM APIs: Tokenization, system prompt steering, streaming completion tokens, and chat history schema.
- Document Ingestion & Vector Embeddings: Parsing PDFs, markdown, and web content into semantic chunks and indexing into vector databases.
- Production RAG & Re-Ranking Pipelines: Eliminating hallucinations with contextual retrieval, citations, and cross-encoder re-ranking.
- Full-Stack Enterprise AI Assistant Capstone: Deploying a secure company knowledge-base assistant with document uploads and role-based permissions.
What you will learn
- Advanced Retrieval-Augmented Generation (RAG) architecture and chunking
- Vector databases: Qdrant, Pinecone, and PGVector indexing
- Hybrid search (BM25 + Dense vector embeddings) with cross-encoder re-ranking
- Conversational memory management: buffer, summary, and entity memory
- Building responsive streaming chat UIs with React and WebSockets
Register for AI Chatbot Development