AI Courses to Build, Deploy & Scale Real-World Applications

Learn how to build a Retrieval-Augmented Generation (RAG) system from scratch by implementing every component of the pipeline. This course explains how large language models connect with external knowledge sources using document processing, chunking strategies, vector embeddings, similarity search, and prompt grounding to produce accurate and context-aware answers.

Learn how to build metadata-aware retrieval systems that power reliable Retrieval-Augmented Generation (RAG) applications. This course teaches how to combine vector search with structured metadata filters such as roles, document types, timestamps, and departments to improve retrieval accuracy and production reliability.

Learn how to design effective document chunking strategies for AI retrieval systems and RAG pipelines. This course teaches how chunk size, overlap, semantic segmentation, and structure-aware chunking influence retrieval quality and the performance of large language model applications.

Master Large Language Models (LLMs) by building real-world AI applications from the ground up. Learn tokenization, embeddings, semantic search, and RAG pipelines while developing a production-ready AI agent using APIs.

Learn how to build high-performance retrieval systems for AI applications using hybrid search and re-ranking techniques. Master RAG pipelines, evaluation metrics, and debugging strategies to deliver accurate, grounded AI answers.

Learn how to build schema-aware retrieval systems that go beyond basic vector search. Handle structured queries, filters, and real-world data using advanced RAG pipelines and multi-index architectures.

Learn how to systematically evaluate and improve RAG (Retrieval-Augmented Generation) systems using metrics, golden datasets, and LLM-as-judge frameworks to build reliable, production-ready AI applications.

Master the art of building robust applications using LangChain. This course bridges the gap between theory and practice, offering hands-on sessions, real-world projects, and expert insights.

Learn how to reduce LLM costs by 40–90% using prompt optimization, model routing, caching, and monitoring. Build scalable, cost-efficient AI systems for production.

Master the Model Context Protocol (MCP) to build AI systems that connect with real-world tools, APIs, and data. Learn agentic workflows, protocol architecture, and production-ready AI integrations.

Learn how to monitor, debug, and scale LLM applications using LangSmith and Langfuse. Build production-ready observability pipelines, trace debugging systems, and incident response workflows for AI apps.

Learn how to containerize and deploy AI applications using Docker. Build scalable multi-service AI systems with Docker Compose, optimize images, and ship production-ready AI backends.